Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

ABSTRACT

Disclosed herein are a point cloud data transmission method including encoding point cloud data, and transmitting a bitstream containing the point cloud data, and a point cloud data reception method including receiving a bitstream containing point cloud data, and decoding the point cloud data.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalApplication No. 63/010,011, filed on Apr. 14, 2020, the contents ofwhich is hereby incorporated by reference as if fully set forth herein.

TECHNICAL FIELD

Embodiments relate to a method and device for processing point cloudcontent.

BACKGROUND

Point cloud content is content represented by a point cloud, which is aset of points belonging to a coordinate system representing athree-dimensional space. The point cloud content may express mediaconfigured in three dimensions, and is used to provide various servicessuch as virtual reality (VR), augmented reality (AR), mixed reality(MR), and self-driving services. However, tens of thousands to hundredsof thousands of point data are required to represent point cloudcontent. Therefore, there is a need for a method for efficientlyprocessing a large amount of point data.

SUMMARY

Embodiments provide a device and method for efficiently processing pointcloud data. Embodiments provide a point cloud data processing method anddevice for addressing latency and encoding/decoding complexity.

The technical scope of the embodiments is not limited to theaforementioned technical objects, and may be extended to other technicalobjects that may be inferred by those skilled in the art based on theentire contents disclosed herein.

To achieve these objects and other advantages and in accordance with thepurpose of the disclosure, as embodied and broadly described herein, amethod for transmitting point cloud data may include encoding the pointcloud data, and transmitting a bitstream containing the point clouddata.

In another aspect of the present disclosure, a method for receivingpoint cloud data may include receiving a bitstream containing the pointcloud data, and decoding the point cloud data.

Devices and methods according to embodiments may process point clouddata with high efficiency.

The devices and methods according to the embodiments may provide ahigh-quality point cloud service.

The devices and methods according to the embodiments may provide pointcloud content for providing general-purpose services such as a VRservice and a self-driving service.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this application, illustrate embodiment(s) of the disclosure andtogether with the description serve to explain the principle of thedisclosure. For a better understanding of various embodiments describedbelow, reference should be made to the description of the followingembodiments in connection with the accompanying drawings. The samereference numbers will be used throughout the drawings to refer to thesame or like parts. In the drawings:

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments;

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments;

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments;

FIG. 4 illustrates an exemplary point cloud encoder according toembodiments;

FIG. 5 shows an example of voxels according to embodiments;

FIG. 6 shows an example of an octree and occupancy code according toembodiments;

FIG. 7 shows an example of a neighbor node pattern according toembodiments;

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments;

FIG. 9 illustrates an example of point configuration in each LODaccording to embodiments;

FIG. 10 illustrates a point cloud decoder according to embodiments;

FIG. 11 illustrates a point cloud decoder according to embodiments;

FIG. 12 illustrates a transmission device according to embodiments;

FIG. 13 illustrates a reception device according to embodiments;

FIG. 14 illustrates an exemplary structure operable in connection withpoint cloud data transmission/reception methods/devices according toembodiments;

FIG. 15 illustrates attribute encoding and attribute decoding accordingto embodiments;

FIG. 16 illustrates a method of calculating a score of a predictoraccording to embodiments;

FIG. 17 illustrates a predictor selection method according toembodiments;

FIG. 18 illustrates a score calculation method according to embodiments;

FIG. 19 illustrates a score calculation method according to embodiments;

FIG. 20 illustrates a score calculation method according to embodiments;

FIG. 21 illustrates a PCC data encoder according to embodiments;

FIG. 22 illustrates a PCC data decoder according to embodiments;

FIG. 23 illustrates a structure of a bitstream according to embodiments;

FIG. 24 shows a sequence parameter set (SPS) according to embodiments;

FIG. 25 shows an attribute parameter set (APS) according to embodiments;

FIG. 26 shows a tile parameter set (TPS) according to embodiments;

FIG. 27 shows an attribute slice header (ASH) according to embodiments;

FIG. 28 illustrates a method for transmitting point cloud data accordingto embodiments; and

FIG. 29 illustrates a method for receiving point cloud data according toembodiments.

DETAILED DESCRIPTION Best Mode

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. The detailed description, which will be givenbelow with reference to the accompanying drawings, is intended toexplain exemplary embodiments of the present disclosure, rather than toshow the only embodiments that may be implemented according to thepresent disclosure. The following detailed description includes specificdetails in order to provide a thorough understanding of the presentdisclosure. However, it will be apparent to those skilled in the artthat the present disclosure may be practiced without such specificdetails.

Although most terms used in the present disclosure have been selectedfrom general ones widely used in the art, some terms have beenarbitrarily selected by the applicant and their meanings are explainedin detail in the following description as needed. Thus, the presentdisclosure should be understood based upon the intended meanings of theterms rather than their simple names or meanings.

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments.

The point cloud content providing system illustrated in FIG. 1 mayinclude a transmission device 10000 and a reception device 10004. Thetransmission device 10000 and the reception device 10004 are capable ofwired or wireless communication to transmit and receive point clouddata.

The point cloud data transmission device 10000 according to theembodiments may secure and process point cloud video (or point cloudcontent) and transmit the same. According to embodiments, thetransmission device 10000 may include a fixed station, a basetransceiver system (BTS), a network, an artificial intelligence (AI)device and/or system, a robot, an AR/VR/XR device and/or server.According to embodiments, the transmission device 10000 may include adevice, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Thing (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The transmission device 10000 according to the embodiments includes apoint cloud video acquirer 10001, a point cloud video encoder 10002,and/or a transmitter (or communication module) 10003.

The point cloud video acquirer 10001 according to the embodimentsacquires a point cloud video through a processing process such ascapture, synthesis, or generation. The point cloud video is point cloudcontent represented by a point cloud, which is a set of pointspositioned in a 3D space, and may be referred to as point cloud videodata. The point cloud video according to the embodiments may include oneor more frames. One frame represents a still image/picture. Therefore,the point cloud video may include a point cloud image/frame/picture, andmay be referred to as a point cloud image, frame, or picture.

The point cloud video encoder 10002 according to the embodiments encodesthe acquired point cloud video data. The point cloud video encoder 10002may encode the point cloud video data based on point cloud compressioncoding. The point cloud compression coding according to the embodimentsmay include geometry-based point cloud compression (G-PCC) coding and/orvideo-based point cloud compression (V-PCC) coding or next-generationcoding. The point cloud compression coding according to the embodimentsis not limited to the above-described embodiment. The point cloud videoencoder 10002 may output a bitstream containing the encoded point cloudvideo data. The bitstream may contain not only the encoded point cloudvideo data, but also signaling information related to encoding of thepoint cloud video data.

The transmitter 10003 according to the embodiments transmits thebitstream containing the encoded point cloud video data. The bitstreamaccording to the embodiments is encapsulated in a file or segment (forexample, a streaming segment), and is transmitted over various networkssuch as a broadcasting network and/or a broadband network. Although notshown in the figure, the transmission device 10000 may include anencapsulator (or an encapsulation module) configured to perform anencapsulation operation. According to embodiments, the encapsulator maybe included in the transmitter 10003. According to embodiments, the fileor segment may be transmitted to the reception device 10004 over anetwork, or stored in a digital storage medium (e.g., USB, SD, CD, DVD,Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to theembodiments is capable of wired/wireless communication with thereception device 10004 (or the receiver 10005) over a network of 4G, 5G,6G, etc. In addition, the transmitter may perform a necessary dataprocessing operation according to the network system (e.g., a 4G, 5G or6G communication network system). The transmission device 10000 maytransmit the encapsulated data in an on-demand manner.

The reception device 10004 according to the embodiments includes areceiver 10005, a point cloud video decoder 10006, and/or a renderer10007. According to embodiments, the reception device 10004 may includea device, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Things (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The receiver 10005 according to the embodiments receives the bitstreamcontaining the point cloud video data or the file/segment in which thebitstream is encapsulated from the network or storage medium. Thereceiver 10005 may perform necessary data processing according to thenetwork system (for example, a communication network system of 4G, 5G,6G, etc.). The receiver 10005 according to the embodiments maydecapsulate the received file/segment and output a bitstream. Accordingto embodiments, the receiver 10005 may include a decapsulator (or adecapsulation module) configured to perform a decapsulation operation.The decapsulator may be implemented as an element (or component)separate from the receiver 10005.

The point cloud video decoder 10006 decodes the bitstream containing thepoint cloud video data. The point cloud video decoder 10006 may decodethe point cloud video data according to the method by which the pointcloud video data is encoded (for example, in a reverse process of theoperation of the point cloud video encoder 10002). Accordingly, thepoint cloud video decoder 10006 may decode the point cloud video data byperforming point cloud decompression coding, which is the inverseprocess of the point cloud compression. The point cloud decompressioncoding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. Therenderer 10007 may output point cloud content by rendering not only thepoint cloud video data but also audio data. According to embodiments,the renderer 10007 may include a display configured to display the pointcloud content. According to embodiments, the display may be implementedas a separate device or component rather than being included in therenderer 10007.

The arrows indicated by dotted lines in the drawing represent atransmission path of feedback information acquired by the receptiondevice 10004. The feedback information is information for reflectinginteractivity with a user who consumes the point cloud content, andincludes information about the user (e.g., head orientation information,viewport information, and the like). In particular, when the point cloudcontent is content for a service (e.g., self-driving service, etc.) thatrequires interaction with the user, the feedback information may beprovided to the content transmitting side (e.g., the transmission device10000) and/or the service provider. According to embodiments, thefeedback information may be used in the reception device 10004 as wellas the transmission device 10000, or may not be provided.

The head orientation information according to embodiments is informationabout the user's head position, orientation, angle, motion, and thelike. The reception device 10004 according to the embodiments maycalculate the viewport information based on the head orientationinformation. The viewport information may be information about a regionof a point cloud video that the user is viewing. A viewpoint is a pointthrough which the user is viewing the point cloud video, and may referto a center point of the viewport region. That is, the viewport is aregion centered on the viewpoint, and the size and shape of the regionmay be determined by a field of view (FOV). Accordingly, the receptiondevice 10004 may extract the viewport information based on a vertical orhorizontal FOV supported by the device in addition to the headorientation information. Also, the reception device 10004 performs gazeanalysis or the like to check the way the user consumes a point cloud, aregion that the user gazes at in the point cloud video, a gaze time, andthe like. According to embodiments, the reception device 10004 maytransmit feedback information including the result of the gaze analysisto the transmission device 10000. The feedback information according tothe embodiments may be acquired in the rendering and/or display process.The feedback information according to the embodiments may be secured byone or more sensors included in the reception device 10004. According toembodiments, the feedback information may be secured by the renderer10007 or a separate external element (or device, component, or thelike). The dotted lines in FIG. 1 represent a process of transmittingthe feedback information secured by the renderer 10007. The point cloudcontent providing system may process (encode/decode) point cloud databased on the feedback information. Accordingly, the point cloud videodata decoder 10006 may perform a decoding operation based on thefeedback information. The reception device 10004 may transmit thefeedback information to the transmission device 10000. The transmissiondevice 10000 (or the point cloud video data encoder 10002) may performan encoding operation based on the feedback information. Accordingly,the point cloud content providing system may efficiently processnecessary data (e.g., point cloud data corresponding to the user's headposition) based on the feedback information rather than processing(encoding/decoding) the entire point cloud data, and provide point cloudcontent to the user.

According to embodiments, the transmission device 10000 may be called anencoder, a transmission device, a transmitter, or the like, and thereception device 10004 may be called a decoder, a receiving device, areceiver, or the like.

The point cloud data processed in the point cloud content providingsystem of FIG. 1 according to embodiments (through a series of processesof acquisition/encoding/transmission/decoding/rendering) may be referredto as point cloud content data or point cloud video data. According toembodiments, the point cloud content data may be used as a conceptcovering metadata or signaling information related to the point clouddata.

The elements of the point cloud content providing system illustrated inFIG. 1 may be implemented by hardware, software, a processor, and/or acombination thereof.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

The block diagram of FIG. 2 shows the operation of the point cloudcontent providing system described in FIG. 1. As described above, thepoint cloud content providing system may process point cloud data basedon point cloud compression coding (e.g., G-PCC).

The point cloud content providing system according to the embodiments(for example, the point cloud transmission device 10000 or the pointcloud video acquirer 10001) may acquire a point cloud video (20000). Thepoint cloud video is represented by a point cloud belonging to acoordinate system for expressing a 3D space. The point cloud videoaccording to the embodiments may include a Ply (Polygon File format orthe Stanford Triangle format) file. When the point cloud video has oneor more frames, the acquired point cloud video may include one or morePly files. The Ply files contain point cloud data, such as pointgeometry and/or attributes. The geometry includes positions of points.The position of each point may be represented by parameters (forexample, values of the X, Y, and Z axes) representing athree-dimensional coordinate system (e.g., a coordinate system composedof X, Y and Z axes). The attributes include attributes of points (e.g.,information about texture, color (in YCbCr or RGB), reflectance r,transparency, etc. of each point). A point has one or more attributes.For example, a point may have an attribute that is a color, or twoattributes that are color and reflectance. According to embodiments, thegeometry may be called positions, geometry information, geometry data,or the like, and the attribute may be called attributes, attributeinformation, attribute data, or the like. The point cloud contentproviding system (for example, the point cloud transmission device 10000or the point cloud video acquirer 10001) may secure point cloud datafrom information (e.g., depth information, color information, etc.)related to the acquisition process of the point cloud video.

The point cloud content providing system (for example, the transmissiondevice 10000 or the point cloud video encoder 10002) according to theembodiments may encode the point cloud data (20001). The point cloudcontent providing system may encode the point cloud data based on pointcloud compression coding. As described above, the point cloud data mayinclude the geometry and attributes of a point. Accordingly, the pointcloud content providing system may perform geometry encoding of encodingthe geometry and output a geometry bitstream. The point cloud contentproviding system may perform attribute encoding of encoding attributesand output an attribute bitstream. According to embodiments, the pointcloud content providing system may perform the attribute encoding basedon the geometry encoding. The geometry bitstream and the attributebitstream according to the embodiments may be multiplexed and output asone bitstream. The bitstream according to the embodiments may furthercontain signaling information related to the geometry encoding andattribute encoding.

The point cloud content providing system (for example, the transmissiondevice 10000 or the transmitter 10003) according to the embodiments maytransmit the encoded point cloud data (20002). As illustrated in FIG. 1,the encoded point cloud data may be represented by a geometry bitstreamand an attribute bitstream. In addition, the encoded point cloud datamay be transmitted in the form of a bitstream together with signalinginformation related to encoding of the point cloud data (for example,signaling information related to the geometry encoding and the attributeencoding). The point cloud content providing system may encapsulate abitstream that carries the encoded point cloud data and transmit thesame in the form of a file or segment.

The point cloud content providing system (for example, the receptiondevice 10004 or the receiver 10005) according to the embodiments mayreceive the bitstream containing the encoded point cloud data. Inaddition, the point cloud content providing system (for example, thereception device 10004 or the receiver 10005) may demultiplex thebitstream.

The point cloud content providing system (e.g., the reception device10004 or the point cloud video decoder 10005) may decode the encodedpoint cloud data (e.g., the geometry bitstream, the attribute bitstream)transmitted in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the point cloud video data based on thesignaling information related to encoding of the point cloud video datacontained in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the geometry bitstream to reconstruct thepositions (geometry) of points. The point cloud content providing systemmay reconstruct the attributes of the points by decoding the attributebitstream based on the reconstructed geometry. The point cloud contentproviding system (for example, the reception device 10004 or the pointcloud video decoder 10005) may reconstruct the point cloud video basedon the positions according to the reconstructed geometry and the decodedattributes.

The point cloud content providing system according to the embodiments(for example, the reception device 10004 or the renderer 10007) mayrender the decoded point cloud data (20004). The point cloud contentproviding system (for example, the reception device 10004 or therenderer 10007) may render the geometry and attributes decoded throughthe decoding process, using various rendering methods. Points in thepoint cloud content may be rendered to a vertex having a certainthickness, a cube having a specific minimum size centered on thecorresponding vertex position, or a circle centered on the correspondingvertex position. All or part of the rendered point cloud content isprovided to the user through a display (e.g., a VR/AR display, a generaldisplay, etc.).

The point cloud content providing system (for example, the receptiondevice 10004) according to the embodiments may secure feedbackinformation (20005). The point cloud content providing system may encodeand/or decode point cloud data based on the feedback information. Thefeedback information and the operation of the point cloud contentproviding system according to the embodiments are the same as thefeedback information and the operation described with reference to FIG.1, and thus detailed description thereof is omitted.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 3 illustrates an exemplary point cloud video capture process of thepoint cloud content providing system described with reference to FIGS. 1to 2.

Point cloud content includes a point cloud video (images and/or videos)representing an object and/or environment located in various 3D spaces(e.g., a 3D space representing a real environment, a 3D spacerepresenting a virtual environment, etc.). Accordingly, the point cloudcontent providing system according to the embodiments may capture apoint cloud video using one or more cameras (e.g., an infrared cameracapable of securing depth information, an RGB camera capable ofextracting color information corresponding to the depth information,etc.), a projector (e.g., an infrared pattern projector to secure depthinformation), a LiDAR, or the like. The point cloud content providingsystem according to the embodiments may extract the shape of geometrycomposed of points in a 3D space from the depth information and extractthe attributes of each point from the color information to secure pointcloud data. An image and/or video according to the embodiments may becaptured based on at least one of the inward-facing technique and theoutward-facing technique.

The left part of FIG. 3 illustrates the inward-facing technique. Theinward-facing technique refers to a technique of capturing images acentral object with one or more cameras (or camera sensors) positionedaround the central object. The inward-facing technique may be used togenerate point cloud content providing a 360-degree image of a keyobject to the user (e.g., VR/AR content providing a 360-degree image ofan object (e.g., a key object such as a character, player, object, oractor) to the user).

The right part of FIG. 3 illustrates the outward-facing technique. Theoutward-facing technique refers to a technique of capturing images anenvironment of a central object rather than the central object with oneor more cameras (or camera sensors) positioned around the centralobject. The outward-facing technique may be used to generate point cloudcontent for providing a surrounding environment that appears from theuser's point of view (e.g., content representing an external environmentthat may be provided to a user of a self-driving vehicle).

As shown in the figure, the point cloud content may be generated basedon the capturing operation of one or more cameras. In this case, thecoordinate system may differ among the cameras, and accordingly thepoint cloud content providing system may calibrate one or more camerasto set a global coordinate system before the capturing operation. Inaddition, the point cloud content providing system may generate pointcloud content by synthesizing an arbitrary image and/or video with animage and/or video captured by the above-described capture technique.The point cloud content providing system may not perform the capturingoperation described in FIG. 3 when it generates point cloud contentrepresenting a virtual space. The point cloud content providing systemaccording to the embodiments may perform post-processing on the capturedimage and/or video. In other words, the point cloud content providingsystem may remove an unwanted area (for example, a background),recognize a space to which the captured images and/or videos areconnected, and, when there is a spatial hole, perform an operation offilling the spatial hole.

The point cloud content providing system may generate one piece of pointcloud content by performing coordinate transformation on points of thepoint cloud video secured from each camera. The point cloud contentproviding system may perform coordinate transformation on the pointsbased on the coordinates of the position of each camera. Accordingly,the point cloud content providing system may generate contentrepresenting one wide range, or may generate point cloud content havinga high density of points.

FIG. 4 illustrates an exemplary point cloud encoder according toembodiments.

FIG. 4 shows an example of the point cloud video encoder 10002 ofFIG. 1. The point cloud encoder reconstructs and encodes point clouddata (e.g., positions and/or attributes of the points) to adjust thequality of the point cloud content (to, for example, lossless, lossy, ornear-lossless) according to the network condition or applications. Whenthe overall size of the point cloud content is large (e.g., point cloudcontent of 60 Gbps is given for 30 fps), the point cloud contentproviding system may fail to stream the content in real time.Accordingly, the point cloud content providing system may reconstructthe point cloud content based on the maximum target bitrate to providethe same in accordance with the network environment or the like.

As described with reference to FIGS. 1 to 2, the point cloud encoder mayperform geometry encoding and attribute encoding. The geometry encodingis performed before the attribute encoding.

The point cloud encoder according to the embodiments includes acoordinate transformer (Transform coordinates) 40000, a quantizer(Quantize and remove points (voxelize)) 40001, an octree analyzer(Analyze octree) 40002, and a surface approximation analyzer (Analyzesurface approximation) 40003, an arithmetic encoder (Arithmetic encode)40004, a geometric reconstructor (Reconstruct geometry) 40005, a colortransformer (Transform colors) 40006, an attribute transformer(Transform attributes) 40007, a RAHT transformer (RAHT) 40008, an LODgenerator (Generate LOD) 40009, a lifting transformer (Lifting) 40010, acoefficient quantizer (Quantize coefficients) 40011, and/or anarithmetic encoder (Arithmetic encode) 40012.

The coordinate transformer 40000, the quantizer 40001, the octreeanalyzer 40002, the surface approximation analyzer 40003, the arithmeticencoder 40004, and the geometry reconstructor 40005 may perform geometryencoding. The geometry encoding according to the embodiments may includeoctree geometry coding, direct coding, trisoup geometry encoding, andentropy encoding. The direct coding and trisoup geometry encoding areapplied selectively or in combination. The geometry encoding is notlimited to the above-described example.

As shown in the figure, the coordinate transformer 40000 according tothe embodiments receives positions and transforms the same intocoordinates. For example, the positions may be transformed into positioninformation in a three-dimensional space (for example, athree-dimensional space represented by an XYZ coordinate system). Theposition information in the three-dimensional space according to theembodiments may be referred to as geometry information.

The quantizer 40001 according to the embodiments quantizes the geometry.For example, the quantizer 40001 may quantize the points based on aminimum position value of all points (for example, a minimum value oneach of the X, Y, and Z axes). The quantizer 40001 performs aquantization operation of multiplying the difference between the minimumposition value and the position value of each point by a presetquantization scale value and then finding the nearest integer value byrounding the value obtained through the multiplication. Thus, one ormore points may have the same quantized position (or position value).The quantizer 40001 according to the embodiments performs voxelizationbased on the quantized positions to reconstruct quantized points. As inthe case of a pixel, which is the minimum unit containing 2D image/videoinformation, points of point cloud content (or 3D point cloud video)according to the embodiments may be included in one or more voxels. Theterm voxel, which is a compound of volume and pixel, refers to a 3Dcubic space generated when a 3D space is divided into units (unit=1.0)based on the axes representing the 3D space (e.g., X-axis, Y-axis, andZ-axis). The quantizer 40001 may match groups of points in the 3D spacewith voxels. According to embodiments, one voxel may include only onepoint. According to embodiments, one voxel may include one or morepoints. In order to express one voxel as one point, the position of thecenter of a voxel may be set based on the positions of one or morepoints included in the voxel. In this case, attributes of all positionsincluded in one voxel may be combined and assigned to the voxel.

The octree analyzer 40002 according to the embodiments performs octreegeometry coding (or octree coding) to present voxels in an octreestructure. The octree structure represents points matched with voxels,based on the octal tree structure.

The surface approximation analyzer 40003 according to the embodimentsmay analyze and approximate the octree. The octree analysis andapproximation according to the embodiments is a process of analyzing aregion containing a plurality of points to efficiently provide octreeand voxelization.

The arithmetic encoder 40004 according to the embodiments performsentropy encoding on the octree and/or the approximated octree. Forexample, the encoding scheme includes arithmetic encoding. As a resultof the encoding, a geometry bitstream is generated.

The color transformer 40006, the attribute transformer 40007, the RAHTtransformer 40008, the LOD generator 40009, the lifting transformer40010, the coefficient quantizer 40011, and/or the arithmetic encoder40012 perform attribute encoding. As described above, one point may haveone or more attributes. The attribute encoding according to theembodiments is equally applied to the attributes that one point has.However, when an attribute (e.g., color) includes one or more elements,attribute encoding is independently applied to each element. Theattribute encoding according to the embodiments includes color transformcoding, attribute transform coding, region adaptive hierarchicaltransform (RAHT) coding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) coding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) coding. Depending on the pointcloud content, the RAHT coding, the prediction transform coding and thelifting transform coding described above may be selectively used, or acombination of one or more of the coding schemes may be used. Theattribute encoding according to the embodiments is not limited to theabove-described example.

The color transformer 40006 according to the embodiments performs colortransform coding of transforming color values (or textures) included inthe attributes. For example, the color transformer 40006 may transformthe format of color information (for example, from RGB to YCbCr). Theoperation of the color transformer 40006 according to embodiments may beoptionally applied according to the color values included in theattributes.

The geometry reconstructor 40005 according to the embodimentsreconstructs (decompresses) the octree and/or the approximated octree.The geometry reconstructor 40005 reconstructs the octree/voxels based onthe result of analyzing the distribution of points. The reconstructedoctree/voxels may be referred to as reconstructed geometry (restoredgeometry).

The attribute transformer 40007 according to the embodiments performsattribute transformation to transform the attributes based on thereconstructed geometry and/or the positions on which geometry encodingis not performed. As described above, since the attributes are dependenton the geometry, the attribute transformer 40007 may transform theattributes based on the reconstructed geometry information. For example,based on the position value of a point included in a voxel, theattribute transformer 40007 may transform the attribute of the point atthe position. As described above, when the position of the center of avoxel is set based on the positions of one or more points included inthe voxel, the attribute transformer 40007 transforms the attributes ofthe one or more points. When the trisoup geometry encoding is performed,the attribute transformer 40007 may transform the attributes based onthe trisoup geometry encoding.

The attribute transformer 40007 may perform the attribute transformationby calculating the average of attributes or attribute values ofneighboring points (e.g., color or reflectance of each point) within aspecific position/radius from the position (or position value) of thecenter of each voxel. The attribute transformer 40007 may apply a weightaccording to the distance from the center to each point in calculatingthe average. Accordingly, each voxel has a position and a calculatedattribute (or attribute value).

The attribute transformer 40007 may search for neighboring pointsexisting within a specific position/radius from the position of thecenter of each voxel based on the K-D tree or the Morton code. The K-Dtree is a binary search tree and supports a data structure capable ofmanaging points based on the positions such that nearest neighbor search(NNS) can be performed quickly. The Morton code is generated bypresenting coordinates (e.g., (x, y, z)) representing 3D positions ofall points as bit values and mixing the bits. For example, when thecoordinates representing the position of a point are (5, 9, 1), the bitvalues for the coordinates are (0101, 1001, 0001). Mixing the bit valuesaccording to the bit index in order of z, y, and x yields 010001000111.This value is expressed as a decimal number of 1095. That is, the Mortoncode value of the point having coordinates (5, 9, 1) is 1095. Theattribute transformer 40007 may order the points based on the Mortoncode values and perform NNS through a depth-first traversal process.After the attribute transformation operation, the K-D tree or the Mortoncode is used when the NNS is needed in another transformation processfor attribute coding.

As shown in the figure, the transformed attributes are input to the RAHTtransformer 40008 and/or the LOD generator 40009.

The RAHT transformer 40008 according to the embodiments performs RAHTcoding for predicting attribute information based on the reconstructedgeometry information. For example, the RAHT transformer 40008 maypredict attribute information of a node at a higher level in the octreebased on the attribute information associated with a node at a lowerlevel in the octree.

The LOD generator 40009 according to the embodiments generates a levelof detail (LOD) to perform prediction transform coding. The LODaccording to the embodiments is a degree of detail of point cloudcontent. As the LOD value decrease, it indicates that the detail of thepoint cloud content is degraded. As the LOD value increases, itindicates that the detail of the point cloud content is enhanced. Pointsmay be classified by the LOD.

The lifting transformer 40010 according to the embodiments performslifting transform coding of transforming the attributes a point cloudbased on weights. As described above, lifting transform coding may beoptionally applied.

The coefficient quantizer 40011 according to the embodiments quantizesthe attribute-coded attributes based on coefficients.

The arithmetic encoder 40012 according to the embodiments encodes thequantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloudencoder of FIG. 4 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud providing device, software,firmware, or a combination thereof. The one or more processors mayperform at least one of the operations and/or functions of the elementsof the point cloud encoder of FIG. 4 described above. Additionally, theone or more processors may operate or execute a set of software programsand/or instructions for performing the operations and/or functions ofthe elements of the point cloud encoder of FIG. 4. The one or morememories according to the embodiments may include a high speed randomaccess memory, or include a non-volatile memory (e.g., one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices).

FIG. 5 shows an example of voxels according to embodiments.

FIG. 5 shows voxels positioned in a 3D space represented by a coordinatesystem composed of three axes, which are the X-axis, the Y-axis, and theZ-axis. As described with reference to FIG. 4, the point cloud encoder(e.g., the quantizer 40001) may perform voxelization. Voxel refers to a3D cubic space generated when a 3D space is divided into units(unit=1.0) based on the axes representing the 3D space (e.g., X-axis,Y-axis, and Z-axis). FIG. 5 shows an example of voxels generated throughan octree structure in which a cubical axis-aligned bounding box definedby two poles (0, 0, 0) and (2d, 2d, 2d) is recursively subdivided. Onevoxel includes at least one point. The spatial coordinates of a voxelmay be estimated from the positional relationship with a voxel group. Asdescribed above, a voxel has an attribute (such as color or reflectance)like pixels of a 2D image/video. The details of the voxel are the sameas those described with reference to FIG. 4, and therefore a descriptionthereof is omitted.

FIG. 6 shows an example of an octree and occupancy code according toembodiments.

As described with reference to FIGS. 1 to 4, the point cloud contentproviding system (point cloud video encoder 10002) or the point cloudencoder (for example, the octree analyzer 40002) performs octreegeometry coding (or octree coding) based on an octree structure toefficiently manage the region and/or position of the voxel.

The upper part of FIG. 6 shows an octree structure. The 3D space of thepoint cloud content according to the embodiments is represented by axes(e.g., X-axis, Y-axis, and Z-axis) of the coordinate system. The octreestructure is created by recursive subdividing of a cubical axis-alignedbounding box defined by two poles (0, 0, 0) and (2 ^(d), 2d, 2d). Here,2d may be set to a value constituting the smallest bounding boxsurrounding all points of the point cloud content (or point cloudvideo). Here, d denotes the depth of the octree. The value of d isdetermined in the following equation. In the following equation,(x^(int) _(n), y^(int) _(n), z^(int) _(n)) denotes the positions (orposition values) of quantized points.

d=Ceil(Log 2(Max(x _(n) ^(init) ,y _(n) ^(init) ,z _(n) ^(init) ,n=1, .. . ,N)+1))

As shown in the middle of the upper part of FIG. 6, the entire 3D spacemay be divided into eight spaces according to partition. Each dividedspace is represented by a cube with six faces. As shown in the upperright of FIG. 6, each of the eight spaces is divided again based on theaxes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis).Accordingly, each space is divided into eight smaller spaces. Thedivided smaller space is also represented by a cube with six faces. Thispartitioning scheme is applied until the leaf node of the octree becomesa voxel.

The lower part of FIG. 6 shows an octree occupancy code. The occupancycode of the octree is generated to indicate whether each of the eightdivided spaces generated by dividing one space contains at least onepoint. Accordingly, a single occupancy code is represented by eightchild nodes. Each child node represents the occupancy of a dividedspace, and the child node has a value in 1 bit. Accordingly, theoccupancy code is represented as an 8-bit code. That is, when at leastone point is contained in the space corresponding to a child node, thenode is assigned a value of 1. When no point is contained in the spacecorresponding to the child node (the space is empty), the node isassigned a value of 0. Since the occupancy code shown in FIG. 6 is00100001, it indicates that the spaces corresponding to the third childnode and the eighth child node among the eight child nodes each containat least one point. As shown in the figure, each of the third child nodeand the eighth child node has eight child nodes, and the child nodes arerepresented by an 8-bit occupancy code. The figure shows that theoccupancy code of the third child node is 10000111, and the occupancycode of the eighth child node is 01001111. The point cloud encoder (forexample, the arithmetic encoder 40004) according to the embodiments mayperform entropy encoding on the occupancy codes. In order to increasethe compression efficiency, the point cloud encoder may performintra/inter-coding on the occupancy codes. The reception device (forexample, the reception device 10004 or the point cloud video decoder10006) according to the embodiments reconstructs the octree based on theoccupancy codes.

The point cloud encoder (for example, the point cloud encoder of FIG. 4or the octree analyzer 40002) according to the embodiments may performvoxelization and octree coding to store the positions of points.However, points are not always evenly distributed in the 3D space, andaccordingly there may be a specific region in which fewer points arepresent. Accordingly, it is inefficient to perform voxelization for theentire 3D space. For example, when a specific region contains fewpoints, voxelization does not need to be performed in the specificregion.

Accordingly, for the above-described specific region (or a node otherthan the leaf node of the octree), the point cloud encoder according tothe embodiments may skip voxelization and perform direct coding todirectly code the positions of points included in the specific region.The coordinates of a direct coding point according to the embodimentsare referred to as direct coding mode (DCM). The point cloud encoderaccording to the embodiments may also perform trisoup geometry encoding,which is to reconstruct the positions of the points in the specificregion (or node) based on voxels, based on a surface model. The trisoupgeometry encoding is geometry encoding that represents an object as aseries of triangular meshes. Accordingly, the point cloud decoder maygenerate a point cloud from the mesh surface. The direct coding andtrisoup geometry encoding according to the embodiments may beselectively performed. In addition, the direct coding and trisoupgeometry encoding according to the embodiments may be performed incombination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applyingdirect coding should be activated. A node to which direct coding is tobe applied is not a leaf node, and points less than a threshold shouldbe present within a specific node. In addition, the total number ofpoints to which direct coding is to be applied should not exceed apreset threshold. When the conditions above are satisfied, the pointcloud encoder (or the arithmetic encoder 40004) according to theembodiments may perform entropy coding on the positions (or positionvalues) of the points.

The point cloud encoder (for example, the surface approximation analyzer40003) according to the embodiments may determine a specific level ofthe octree (a level less than the depth d of the octree), and thesurface model may be used staring with that level to perform trisoupgeometry encoding to reconstruct the positions of points in the regionof the node based on voxels (Trisoup mode). The point cloud encoderaccording to the embodiments may specify a level at which trisoupgeometry encoding is to be applied. For example, when the specific levelis equal to the depth of the octree, the point cloud encoder does notoperate in the trisoup mode. In other words, the point cloud encoderaccording to the embodiments may operate in the trisoup mode only whenthe specified level is less than the value of depth of the octree. The3D cube region of the nodes at the specified level according to theembodiments is called a block. One block may include one or more voxels.The block or voxel may correspond to a brick. Geometry is represented asa surface within each block. The surface according to embodiments mayintersect with each edge of a block at most once.

One block has 12 edges, and accordingly there are at least 12intersections in one block. Each intersection is called a vertex (orapex). A vertex present along an edge is detected when there is at leastone occupied voxel adjacent to the edge among all blocks sharing theedge. The occupied voxel according to the embodiments refers to a voxelcontaining a point. The position of the vertex detected along the edgeis the average position along the edge of all voxels adjacent to theedge among all blocks sharing the edge.

Once the vertex is detected, the point cloud encoder according to theembodiments may perform entropy encoding on the starting point (x, y, z)of the edge, the direction vector (Δx, Δy, Δz) of the edge, and thevertex position value (relative position value within the edge). Whenthe trisoup geometry encoding is applied, the point cloud encoderaccording to the embodiments (for example, the geometry reconstructor40005) may generate restored geometry (reconstructed geometry) byperforming the triangle reconstruction, up-sampling, and voxelizationprocesses.

The vertices positioned at the edge of the block determine a surfacethat passes through the block. The surface according to the embodimentsis a non-planar polygon. In the triangle reconstruction process, asurface represented by a triangle is reconstructed based on the startingpoint of the edge, the direction vector of the edge, and the positionvalues of the vertices. The triangle reconstruction process is performedby: i) calculating the centroid value of each vertex, ii) subtractingthe center value from each vertex value, and iii) estimating the sum ofthe squares of the values obtained by the subtraction.

${\left. {{{\left. {{{\left. i \right)\begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{z}\end{bmatrix}} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\;\begin{bmatrix}x_{i} \\y_{i} \\z_{i}\end{bmatrix}}}};{ii}} \right)\begin{bmatrix}{\overset{\_}{x}}_{i} \\{\overset{\_}{y}}_{i} \\{\overset{\_}{z}}_{i}\end{bmatrix}} - \begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{Z}\end{bmatrix}};{iii}} \right)\begin{bmatrix}\sigma_{x}^{2} \\\sigma_{y}^{2} \\\sigma_{Z}^{2}\end{bmatrix}} = {\sum\limits_{i = 1}^{n}\begin{bmatrix}{\overset{\_}{x}}_{i}^{2} \\{\overset{\_}{y}}_{i}^{2} \\{\overset{\_}{z}}_{i}\end{bmatrix}}$

The minimum value of the sum is estimated, and the projection process isperformed according to the axis with the minimum value. For example,when the element x is the minimum, each vertex is projected on thex-axis with respect to the center of the block, and projected on the (y,z) plane. When the values obtained through projection on the (y, z)plane are (ai, bi), the value of θ is estimated through atan 2(bi, ai),and the vertices are ordered based on the value of θ. The table belowshows a combination of vertices for creating a triangle according to thenumber of the vertices. The vertices are ordered from 1 to n. The tablebelow shows that for four vertices, two triangles may be constructedaccording to combinations of vertices. The first triangle may consist ofvertices 1, 2, and 3 among the ordered vertices, and the second trianglemay consist of vertices 3, 4, and 1 among the ordered vertices.

TABLE 2-1 Triangles formed from vertices ordered 1, . . . , n ntriangles 3 (1, 2, 3) 4 (1, 2, 3), (3, 4, 1) 5 (1, 2, 3), (3, 4, 5), (5,1, 3) 6 (1, 2, 3), (3, 4, 5), (5, 6, 1), (1, 3, 5) 7 (1, 2, 3), (3, 4,5), (5, 6, 7), (7, 1, 3), (3, 5, 7) 8 (1, 2, 3), (3, 4, 5), (5, 6, 7),(7, 8, 1), (1, 3, 5), (5, 7, 1) 9 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7,8, 9), (9, 1, 3), (3, 5, 7), (7, 9, 3) 10 (1, 2, 3), (3, 4, 5), (5, 6,7), (7, 8, 9), (9, 10, 1), (1, 3, 5), (5, 7, 9), (9, 1, 5) 11 (1, 2, 3),(3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 10, 11), (11, 1, 3), (3, 5, 7), (7,9, 11), (11, 3, 7) 12 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9,10, 11), (11, 12, 1), (1, 3, 5), (5, 7, 9), (9, 11, 1), (1, 5, 9)

The upsampling process is performed to add points in the middle alongthe edge of the triangle and perform voxelization. The added points aregenerated based on the upsampling factor and the width of the block. Theadded points are called refined vertices. The point cloud encoderaccording to the embodiments may voxelize the refined vertices. Inaddition, the point cloud encoder may perform attribute encoding basedon the voxelized positions (or position values).

FIG. 7 shows an example of a neighbor node pattern according toembodiments.

In order to increase the compression efficiency of the point cloudvideo, the point cloud encoder according to the embodiments may performentropy coding based on context adaptive arithmetic coding.

As described with reference to FIGS. 1 to 6, the point cloud contentproviding system or the point cloud encoder (for example, the pointcloud video encoder 10002, the point cloud encoder or arithmetic encoder40004 of FIG. 4) may perform entropy coding on the occupancy codeimmediately. In addition, the point cloud content providing system orthe point cloud encoder may perform entropy encoding (intra encoding)based on the occupancy code of the current node and the occupancy ofneighboring nodes, or perform entropy encoding (inter encoding) based onthe occupancy code of the previous frame. A frame according toembodiments represents a set of point cloud videos generated at the sametime. The compression efficiency of intra encoding/inter encodingaccording to the embodiments may depend on the number of neighboringnodes that are referenced. When the bits increase, the operation becomescomplicated, but the encoding may be biased to one side, which mayincrease the compression efficiency. For example, when a 3-bit contextis given, coding needs to be performed using 23=8 methods. The partdivided for coding affects the complexity of implementation.Accordingly, it is necessary to meet an appropriate level of compressionefficiency and complexity.

FIG. 7 illustrates a process of obtaining an occupancy pattern based onthe occupancy of neighbor nodes. The point cloud encoder according tothe embodiments determines occupancy of neighbor nodes of each node ofthe octree and obtains a value of a neighbor pattern. The neighbor nodepattern is used to infer the occupancy pattern of the node. The leftpart of FIG. 7 shows a cube corresponding to a node (a cube positionedin the middle) and six cubes (neighbor nodes) sharing at least one facewith the cube. The nodes shown in the figure are nodes of the samedepth. The numbers shown in the figure represent weights (1, 2, 4, 8,16, and 32) associated with the six nodes, respectively. The weights areassigned sequentially according to the positions of neighboring nodes.

The right part of FIG. 7 shows neighbor node pattern values. A neighbornode pattern value is the sum of values multiplied by the weight of anoccupied neighbor node (a neighbor node having a point). Accordingly,the neighbor node pattern values are 0 to 63. When the neighbor nodepattern value is 0, it indicates that there is no node having a point(no occupied node) among the neighbor nodes of the node. When theneighbor node pattern value is 63, it indicates that all neighbor nodesare occupied nodes. As shown in the figure, since neighbor nodes towhich weights 1, 2, 4, and 8 are assigned are occupied nodes, theneighbor node pattern value is 15, the sum of 1, 2, 4, and 8. The pointcloud encoder may perform coding according to the neighbor node patternvalue (for example, when the neighbor node pattern value is 63, 64 kindsof coding may be performed). According to embodiments, the point cloudencoder may reduce coding complexity by changing a neighbor node patternvalue (for example, based on a table by which 64 is changed to 10 or 6).

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments.

As described with reference to FIGS. 1 to 7, encoded geometry isreconstructed (decompressed) before attribute encoding is performed.When direct coding is applied, the geometry reconstruction operation mayinclude changing the placement of direct coded points (e.g., placing thedirect coded points in front of the point cloud data). When trisoupgeometry encoding is applied, the geometry reconstruction process isperformed through triangle reconstruction, up-sampling, andvoxelization. Since the attribute depends on the geometry, attributeencoding is performed based on the reconstructed geometry.

The point cloud encoder (for example, the LOD generator 40009) mayclassify (reorganize) points by LOD. The figure shows the point cloudcontent corresponding to LODs. The leftmost picture in the figurerepresents original point cloud content. The second picture from theleft of the figure represents distribution of the points in the lowestLOD, and the rightmost picture in the figure represents distribution ofthe points in the highest LOD. That is, the points in the lowest LOD aresparsely distributed, and the points in the highest LOD are denselydistributed. That is, as the LOD rises in the direction pointed by thearrow indicated at the bottom of the figure, the space (or distance)between points is narrowed.

FIG. 9 illustrates an example of point configuration for each LODaccording to embodiments.

As described with reference to FIGS. 1 to 8, the point cloud contentproviding system, or the point cloud encoder (for example, the pointcloud video encoder 10002, the point cloud encoder of FIG. 4, or the LODgenerator 40009) may generates an LOD. The LOD is generated byreorganizing the points into a set of refinement levels according to aset LOD distance value (or a set of Euclidean distances). The LODgeneration process is performed not only by the point cloud encoder, butalso by the point cloud decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of thepoint cloud content distributed in a 3D space. In FIG. 9, the originalorder represents the order of points P0 to P9 before LOD generation. InFIG. 9, the LOD based order represents the order of points according tothe LOD generation. Points are reorganized by LOD. Also, a high LODcontains the points belonging to lower LODs. As shown in FIG. 9, LOD0contains P0, P5, P4 and P2. LOD1 contains the points of LOD0, P1, P6 andP3. LOD2 contains the points of LOD0, the points of LOD 1, P9, P8 andP7.

As described with reference to FIG. 4, the point cloud encoder accordingto the embodiments may perform prediction transform coding, liftingtransform coding, and RAHT transform coding selectively or incombination.

The point cloud encoder according to the embodiments may generate apredictor for points to perform prediction transform coding for settinga predicted attribute (or predicted attribute value) of each point. Thatis, N predictors may be generated for N points. The predictor accordingto the embodiments may calculate a weight (=1/distance) based on the LODvalue of each point, indexing information about neighboring pointspresent within a set distance for each LOD, and a distance to theneighboring points.

The predicted attribute (or attribute value) according to theembodiments is set to the average of values obtained by multiplying theattributes (or attribute values) (e.g., color, reflectance, etc.) ofneighbor points set in the predictor of each point by a weight (orweight value) calculated based on the distance to each neighbor point.The point cloud encoder according to the embodiments (for example, thecoefficient quantizer 40011) may quantize and inversely quantize theresiduals (which may be called residual attributes, residual attributevalues, or attribute prediction residuals) obtained by subtracting apredicted attribute (attribute value) from the attribute (attributevalue) of each point. The quantization process is configured as shown inthe following table.

TABLE Attribute prediction residuals quantization pseudo code intPCCQuantization(int value, int quantStep) { if( value >=0) { returnfloor(value / quantStep + 1.0 / 3.0); } else { return −floor(−value /quantStep + 1.0 / 3.0);  } }

TABLE Attribute prediction residuals inverse quantization pseudo codeint PCCInverseQuantization(int value, int quantStep) { if( quantStep==0) { return value; } else { return value * quantStep;  } }

When the predictor of each point has neighbor points, the point cloudencoder (e.g., the arithmetic encoder 40012) according to theembodiments may perform entropy coding on the quantized and inverselyquantized residual values as described above. When the predictor of eachpoint has no neighbor point, the point cloud encoder according to theembodiments (for example, the arithmetic encoder 40012) may performentropy coding on the attributes of the corresponding point withoutperforming the above-described operation.

The point cloud encoder according to the embodiments (for example, thelifting transformer 40010) may generate a predictor of each point, setthe calculated LOD and register neighbor points in the predictor, andset weights according to the distances to neighbor points to performlifting transform coding. The lifting transform coding according to theembodiments is similar to the above-described prediction transformcoding, but differs therefrom in that weights are cumulatively appliedto attribute values. The process of cumulatively applying weights to theattribute values according to embodiments is configured as follows.

1) Create an array Quantization Weight (QW) for storing the weight valueof each point. The initial value of all elements of QW is 1.0. Multiplythe QW values of the predictor indexes of the neighbor nodes registeredin the predictor by the weight of the predictor of the current point,and add the values obtained by the multiplication.

2) Lift prediction process: Subtract the value obtained by multiplyingthe attribute value of the point by the weight from the existingattribute value to calculate a predicted attribute value.

3) Create temporary arrays called updateweight and update and initializethe temporary arrays to zero.

4) Cumulatively add the weights calculated by multiplying the weightscalculated for all predictors by a weight stored in the QW correspondingto a predictor index to the updateweight array as indexes of neighbornodes. Cumulatively add, to the update array, a value obtained bymultiplying the attribute value of the index of a neighbor node by thecalculated weight.

5) Lift update process: Divide the attribute values of the update arrayfor all predictors by the weight value of the updateweight array of thepredictor index, and add the existing attribute value to the valuesobtained by the division.

6) Calculate predicted attributes by multiplying the attribute valuesupdated through the lift update process by the weight updated throughthe lift prediction process (stored in the QW) for all predictors. Thepoint cloud encoder (e.g., coefficient quantizer 40011) according to theembodiments quantizes the predicted attribute values. In addition, thepoint cloud encoder (e.g., the arithmetic encoder 40012) performsentropy coding on the quantized attribute values.

The point cloud encoder (for example, the RAHT transformer 40008)according to the embodiments may perform RAHT transform coding in whichattributes of nodes of a higher level are predicted using the attributesassociated with nodes of a lower level in the octree. RAHT transformcoding is an example of attribute intra coding through an octreebackward scan. The point cloud encoder according to the embodimentsscans the entire region from the voxel and repeats the merging processof merging the voxels into a larger block at each step until the rootnode is reached. The merging process according to the embodiments isperformed only on the occupied nodes. The merging process is notperformed on the empty node. The merging process is performed on anupper node immediately above the empty node.

The equation below represents a RAHT transformation matrix. In theequation, g_(l) _(x,y,z) denotes the average attribute value of voxelsat level l. g_(l) _(x,y,z) may be calculated based on g_(l+1) _(2x,y,z)and g_(l+1) _(2x+1,y,z) . The weights for g_(l) _(2x,y,z) and g_(l)_(2x+1,y,z) are w1=w_(l) _(2x,y,z) and w2=w_(l) _(2x+1,y,z) .

${\left\lceil \begin{matrix}g_{l - 1_{x,y,z}} \\h_{l - 1_{x,y,z}}\end{matrix} \right\rceil = {T_{w1w2}\left\lceil \begin{matrix}g_{{2\; x},y,z} \\g_{{2\; + 1},y,z}\end{matrix} \right\rceil}},{T_{w\; 1w\; 2} = {\frac{1}{\sqrt{{w1} + {w2}}}\begin{bmatrix}\sqrt{w1} & \sqrt{w2} \\{- \sqrt{w2}} & \sqrt{w1}\end{bmatrix}}}$

Here, g_(l−1) _(x,y,z) is a low-pass value and is used in the mergingprocess at the next higher level. h_(l−1) _(x,y,z) denotes high-passcoefficients. The high-pass coefficients at each step are quantized andsubjected to entropy coding (for example, encoding by the arithmeticencoder 400012). The weights are calculated as w_(l) _(−1x,y,z) =w_(l)_(2x,y,z) +w_(l) _(2x+1,y,z) . The root node is created through the g₁_(0,0,0) and g₁ _(0,0,1) as follows.

$\left\lceil \begin{matrix}{gDC} \\h_{l - 1_{x,y,z}}\end{matrix} \right\rceil = {T_{w\; 1000\; w\; 1001}\left\lceil \begin{matrix}g_{1_{0,0,{0z}}} \\g_{1_{0,0,1}}\end{matrix} \right\rceil}$

The value of gDC is also quantized and subjected to entropy coding likethe high-pass coefficients.

FIG. 10 illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 10 is an example of thepoint cloud video decoder 10006 described in FIG. 1, and may perform thesame or similar operations as the operations of the point cloud videodecoder 10006 illustrated in FIG. 1. As shown in the figure, the pointcloud decoder may receive a geometry bitstream and an attributebitstream contained in one or more bitstreams. The point cloud decoderincludes a geometry decoder and an attribute decoder. The geometrydecoder performs geometry decoding on the geometry bitstream and outputsdecoded geometry. The attribute decoder performs attribute decodingbased on the decoded geometry and the attribute bitstream, and outputsdecoded attributes. The decoded geometry and decoded attributes are usedto reconstruct point cloud content (a decoded point cloud).

FIG. 11 illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 11 is an example of thepoint cloud decoder illustrated in FIG. 10, and may perform a decodingoperation, which is an inverse process of the encoding operation of thepoint cloud encoder illustrated in FIGS. 1 to 9.

As described with reference to FIGS. 1 and 10, the point cloud decodermay perform geometry decoding and attribute decoding. The geometrydecoding is performed before the attribute decoding.

The point cloud decoder according to the embodiments includes anarithmetic decoder (Arithmetic decode) 11000, an octree synthesizer(Synthesize octree) 11001, a surface approximation synthesizer(Synthesize surface approximation) 11002, and a geometry reconstructor(Reconstruct geometry) 11003, a coordinate inverse transformer (Inversetransform coordinates) 11004, an arithmetic decoder (Arithmetic decode)11005, an inverse quantizer (Inverse quantize) 11006, a RAHT transformer11007, an LOD generator (Generate LOD) 11008, an inverse lifter (inverselifting) 11009, and/or a color inverse transformer (Inverse transformcolors) 11010.

The arithmetic decoder 11000, the octree synthesizer 11001, the surfaceapproximation synthesizer 11002, and the geometry reconstructor 11003,and the coordinate inverse transformer 11004 may perform geometrydecoding. The geometry decoding according to the embodiments may includedirect coding and trisoup geometry decoding. The direct coding andtrisoup geometry decoding are selectively applied. The geometry decodingis not limited to the above-described example, and is performed as aninverse process of the geometry encoding described with reference toFIGS. 1 to 9.

The arithmetic decoder 11000 according to the embodiments decodes thereceived geometry bitstream based on the arithmetic coding. Theoperation of the arithmetic decoder 11000 corresponds to the inverseprocess of the arithmetic encoder 40004.

The octree synthesizer 11001 according to the embodiments may generatean octree by acquiring an occupancy code from the decoded geometrybitstream (or information on the geometry secured as a result ofdecoding). The occupancy code is configured as described in detail withreference to FIGS. 1 to 9.

When the trisoup geometry encoding is applied, the surface approximationsynthesizer 11002 according to the embodiments may synthesize a surfacebased on the decoded geometry and/or the generated octree.

The geometry reconstructor 11003 according to the embodiments mayregenerate geometry based on the surface and/or the decoded geometry. Asdescribed with reference to FIGS. 1 to 9, direct coding and trisoupgeometry encoding are selectively applied. Accordingly, the geometryreconstructor 11003 directly imports and adds position information aboutthe points to which direct coding is applied. When the trisoup geometryencoding is applied, the geometry reconstructor 11003 may reconstructthe geometry by performing the reconstruction operations of the geometryreconstructor 40005, for example, triangle reconstruction, up-sampling,and voxelization. Details are the same as those described with referenceto FIG. 6, and thus description thereof is omitted. The reconstructedgeometry may include a point cloud picture or frame that does notcontain attributes.

The coordinate inverse transformer 11004 according to the embodimentsmay acquire positions of the points by transforming the coordinatesbased on the reconstructed geometry.

The arithmetic decoder 11005, the inverse quantizer 11006, the RAHTtransformer 11007, the LOD generator 11008, the inverse lifter 11009,and/or the color inverse transformer 11010 may perform the attributedecoding described with reference to FIG. 10. The attribute decodingaccording to the embodiments includes region adaptive hierarchicaltransform (RAHT) decoding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) decoding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) decoding. The three decodingschemes described above may be used selectively, or a combination of oneor more decoding schemes may be used. The attribute decoding accordingto the embodiments is not limited to the above-described example.

The arithmetic decoder 11005 according to the embodiments decodes theattribute bitstream by arithmetic coding.

The inverse quantizer 11006 according to the embodiments inverselyquantizes the information about the decoded attribute bitstream orattributes secured as a result of the decoding, and outputs theinversely quantized attributes (or attribute values). The inversequantization may be selectively applied based on the attribute encodingof the point cloud encoder.

According to embodiments, the RAHT transformer 11007, the LOD generator11008, and/or the inverse lifter 11009 may process the reconstructedgeometry and the inversely quantized attributes. As described above, theRAHT transformer 11007, the LOD generator 11008, and/or the inverselifter 11009 may selectively perform a decoding operation correspondingto the encoding of the point cloud encoder.

The color inverse transformer 11010 according to the embodimentsperforms inverse transform coding to inversely transform a color value(or texture) included in the decoded attributes. The operation of thecolor inverse transformer 11010 may be selectively performed based onthe operation of the color transformer 40006 of the point cloud encoder.

Although not shown in the figure, the elements of the point clouddecoder of FIG. 11 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud providing device, software,firmware, or a combination thereof. The one or more processors mayperform at least one or more of the operations and/or functions of theelements of the point cloud decoder of FIG. 11 described above.Additionally, the one or more processors may operate or execute a set ofsoftware programs and/or instructions for performing the operationsand/or functions of the elements of the point cloud decoder of FIG. 11.

FIG. 12 illustrates a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of thetransmission device 10000 of FIG. 1 (or the point cloud encoder of FIG.4). The transmission device illustrated in FIG. 12 may perform one ormore of the operations and methods the same as or similar to those ofthe point cloud encoder described with reference to FIGS. 1 to 9. Thetransmission device according to the embodiments may include a datainput unit 12000, a quantization processor 12001, a voxelizationprocessor 12002, an octree occupancy code generator 12003, a surfacemodel processor 12004, an intra/inter-coding processor 12005, anarithmetic coder 12006, a metadata processor 12007, a color transformprocessor 12008, an attribute transform processor 12009, aprediction/lifting/RAHT transform processor 12010, an arithmetic coder12011 and/or a transmission processor 12012.

The data input unit 12000 according to the embodiments receives oracquires point cloud data. The data input unit 12000 may perform anoperation and/or acquisition method the same as or similar to theoperation and/or acquisition method of the point cloud video acquirer10001 (or the acquisition process 20000 described with reference to FIG.2).

The data input unit 12000, the quantization processor 12001, thevoxelization processor 12002, the octree occupancy code generator 12003,the surface model processor 12004, the intra/inter-coding processor12005, and the arithmetic coder 12006 perform geometry encoding. Thegeometry encoding according to the embodiments is the same as or similarto the geometry encoding described with reference to FIGS. 1 to 9, andthus a detailed description thereof is omitted.

The quantization processor 12001 according to the embodiments quantizesgeometry (e.g., position values of points). The operation and/orquantization of the quantization processor 12001 is the same as orsimilar to the operation and/or quantization of the quantizer 40001described with reference to FIG. 4. Details are the same as thosedescribed with reference to FIGS. 1 to 9.

The voxelization processor 12002 according to the embodiments voxelizesthe quantized position values of the points. The voxelization processor120002 may perform an operation and/or process the same or similar tothe operation and/or the voxelization process of the quantizer 40001described with reference to FIG. 4. Details are the same as thosedescribed with reference to FIGS. 1 to 9.

The octree occupancy code generator 12003 according to the embodimentsperforms octree coding on the voxelized positions of the points based onan octree structure. The octree occupancy code generator 12003 maygenerate an occupancy code. The octree occupancy code generator 12003may perform an operation and/or method the same as or similar to theoperation and/or method of the point cloud encoder (or the octreeanalyzer 40002) described with reference to FIGS. 4 and 6. Details arethe same as those described with reference to FIGS. 1 to 9.

The surface model processor 12004 according to the embodiments mayperform trigsoup geometry encoding based on a surface model toreconstruct the positions of points in a specific region (or node) on avoxel basis. The surface model processor 12004 may perform an operationand/or method the same as or similar to the operation and/or method ofthe point cloud encoder (for example, the surface approximation analyzer40003) described with reference to FIG. 4. Details are the same as thosedescribed with reference to FIGS. 1 to 9.

The intra/inter-coding processor 12005 according to the embodiments mayperform intra/inter-coding on point cloud data. The intra/inter-codingprocessor 12005 may perform coding the same as or similar to theintra/inter-coding described with reference to FIG. 7. Details are thesame as those described with reference to FIG. 7. According toembodiments, the intra/inter-coding processor 12005 may be included inthe arithmetic coder 12006.

The arithmetic coder 12006 according to the embodiments performs entropyencoding on an octree of the point cloud data and/or an approximatedoctree. For example, the encoding scheme includes arithmetic encoding.The arithmetic coder 12006 performs an operation and/or method the sameas or similar to the operation and/or method of the arithmetic encoder40004.

The metadata processor 12007 according to the embodiments processesmetadata about the point cloud data, for example, a set value, andprovides the same to a necessary processing process such as geometryencoding and/or attribute encoding. Also, the metadata processor 12007according to the embodiments may generate and/or process signalinginformation related to the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beencoded separately from the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beinterleaved.

The color transform processor 12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor 12010, and thearithmetic coder 12011 perform the attribute encoding. The attributeencoding according to the embodiments is the same as or similar to theattribute encoding described with reference to FIGS. 1 to 9, and thus adetailed description thereof is omitted.

The color transform processor 12008 according to the embodimentsperforms color transform coding to transform color values included inattributes. The color transform processor 12008 may perform colortransform coding based on the reconstructed geometry. The reconstructedgeometry is the same as described with reference to FIGS. 1 to 9. Also,it performs an operation and/or method the same as or similar to theoperation and/or method of the color transformer 40006 described withreference to FIG. 4 is performed. The detailed description thereof isomitted.

The attribute transform processor 12009 according to the embodimentsperforms attribute transformation to transform the attributes based onthe reconstructed geometry and/or the positions on which geometryencoding is not performed. The attribute transform processor 12009performs an operation and/or method the same as or similar to theoperation and/or method of the attribute transformer 40007 describedwith reference to FIG. 4. The detailed description thereof is omitted.The prediction/lifting/RAHT transform processor 12010 according to theembodiments may code the transformed attributes by any one or acombination of RAHT coding, prediction transform coding, and liftingtransform coding. The prediction/lifting/RAHT transform processor 12010performs at least one of the operations the same as or similar to theoperations of the RAHT transformer 40008, the LOD generator 40009, andthe lifting transformer 40010 described with reference to FIG. 4. Inaddition, the prediction transform coding, the lifting transform coding,and the RAHT transform coding are the same as those described withreference to FIGS. 1 to 9, and thus a detailed description thereof isomitted.

The arithmetic coder 12011 according to the embodiments may encode thecoded attributes based on the arithmetic coding. The arithmetic coder12011 performs an operation and/or method the same as or similar to theoperation and/or method of the arithmetic encoder 400012.

The transmission processor 12012 according to the embodiments maytransmit each bitstream containing encoded geometry and/or encodedattributes and metadata information, or transmit one bitstreamconfigured with the encoded geometry and/or the encoded attributes andthe metadata information. When the encoded geometry and/or the encodedattributes and the metadata information according to the embodiments areconfigured into one bitstream, the bitstream may include one or moresub-bitstreams. The bitstream according to the embodiments may containsignaling information including a sequence parameter set (SPS) forsignaling of a sequence level, a geometry parameter set (GPS) forsignaling of geometry information coding, an attribute parameter set(APS) for signaling of attribute information coding, and a tileparameter set (TPS) for signaling of a tile level, and slice data. Theslice data may include information about one or more slices. One sliceaccording to embodiments may include one geometry bitstream Geom0⁰ andone or more attribute bitstreams Attr0⁰ and Attr1⁰.

A slice refers to a series of syntax elements representing the entiretyor part of a coded point cloud frame.

The TPS according to the embodiments may include information about eachtile (for example, coordinate information and height/size informationabout a bounding box) for one or more tiles. The geometry bitstream maycontain a header and a payload. The header of the geometry bitstreamaccording to the embodiments may contain a parameter set identifier(geomparameter_set_id), a tile identifier (geom_tile_id) and a sliceidentifier (geom_slice_id) included in the GPS, and information aboutthe data contained in the payload. As described above, the metadataprocessor 12007 according to the embodiments may generate and/or processthe signaling information and transmit the same to the transmissionprocessor 12012. According to embodiments, the elements to performgeometry encoding and the elements to perform attribute encoding mayshare data/information with each other as indicated by dotted lines. Thetransmission processor 12012 according to the embodiments may perform anoperation and/or transmission method the same as or similar to theoperation and/or transmission method of the transmitter 10003. Detailsare the same as those described with reference to FIGS. 1 and 2, andthus a description thereof is omitted.

FIG. 13 illustrates a reception device according to embodiments.

The reception device illustrated in FIG. 13 is an example of thereception device 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10and 11). The reception device illustrated in FIG. 13 may perform one ormore of the operations and methods the same as or similar to those ofthe point cloud decoder described with reference to FIGS. 1 to 11.

The reception device according to the embodiment includes a receiver13000, a reception processor 13001, an arithmetic decoder 13002, anoccupancy code-based octree reconstruction processor 13003, a surfacemodel processor (triangle reconstruction, up-sampling, voxelization)13004, an inverse quantization processor 13005, a metadata parser 13006,an arithmetic decoder 13007, an inverse quantization processor 13008, aprediction/lifting/RAHT inverse transform processor 13009, a colorinverse transform processor 13010, and/or a renderer 13011. Each elementfor decoding according to the embodiments may perform an inverse processof the operation of a corresponding element for encoding according tothe embodiments.

The receiver 13000 according to the embodiments receives point clouddata. The receiver 13000 may perform an operation and/or receptionmethod the same as or similar to the operation and/or reception methodof the receiver 10005 of FIG. 1. The detailed description thereof isomitted.

The reception processor 13001 according to the embodiments may acquire ageometry bitstream and/or an attribute bitstream from the received data.The reception processor 13001 may be included in the receiver 13000.

The arithmetic decoder 13002, the occupancy code-based octreereconstruction processor 13003, the surface model processor 13004, andthe inverse quantization processor 1305 may perform geometry decoding.The geometry decoding according to embodiments is the same as or similarto the geometry decoding described with reference to FIGS. 1 to 10, andthus a detailed description thereof is omitted.

The arithmetic decoder 13002 according to the embodiments may decode thegeometry bitstream based on arithmetic coding. The arithmetic decoder13002 performs an operation and/or coding the same as or similar to theoperation and/or coding of the arithmetic decoder 11000.

The occupancy code-based octree reconstruction processor 13003 accordingto the embodiments may reconstruct an octree by acquiring an occupancycode from the decoded geometry bitstream (or information about thegeometry secured as a result of decoding). The occupancy code-basedoctree reconstruction processor 13003 performs an operation and/ormethod the same as or similar to the operation and/or octree generationmethod of the octree synthesizer 11001. When the trisoup geometryencoding is applied, the surface model processor 1302 according to theembodiments may perform trisoup geometry decoding and related geometryreconstruction (for example, triangle reconstruction, up-sampling,voxelization) based on the surface model method. The surface modelprocessor 1302 performs an operation the same as or similar to that ofthe surface approximation synthesizer 11002 and/or the geometryreconstructor 11003.

The inverse quantization processor 1305 according to the embodiments mayinversely quantize the decoded geometry.

The metadata parser 1306 according to the embodiments may parse metadatacontained in the received point cloud data, for example, a set value.The metadata parser 1306 may pass the metadata to geometry decodingand/or attribute decoding. The metadata is the same as that describedwith reference to FIG. 12, and thus a detailed description thereof isomitted.

The arithmetic decoder 13007, the inverse quantization processor 13008,the prediction/lifting/RAHT inverse transform processor 13009 and thecolor inverse transform processor 13010 perform attribute decoding. Theattribute decoding is the same as or similar to the attribute decodingdescribed with reference to FIGS. 1 to 10, and thus a detaileddescription thereof is omitted.

The arithmetic decoder 13007 according to the embodiments may decode theattribute bitstream by arithmetic coding. The arithmetic decoder 13007may decode the attribute bitstream based on the reconstructed geometry.The arithmetic decoder 13007 performs an operation and/or coding thesame as or similar to the operation and/or coding of the arithmeticdecoder 11005.

The inverse quantization processor 13008 according to the embodimentsmay inversely quantize the decoded attribute bitstream. The inversequantization processor 13008 performs an operation and/or method thesame as or similar to the operation and/or inverse quantization methodof the inverse quantizer 11006.

The prediction/lifting/RAHT inverse transformer 13009 according to theembodiments may process the reconstructed geometry and the inverselyquantized attributes. The prediction/lifting/RAHT inverse transformprocessor 1301 performs one or more of operations and/or decoding thesame as or similar to the operations and/or decoding of the RAHTtransformer 11007, the LOD generator 11008, and/or the inverse lifter11009. The color inverse transform processor 13010 according to theembodiments performs inverse transform coding to inversely transformcolor values (or textures) included in the decoded attributes. The colorinverse transform processor 13010 performs an operation and/or inversetransform coding the same as or similar to the operation and/or inversetransform coding of the color inverse transformer 11010. The renderer13011 according to the embodiments may render the point cloud data.

FIG. 14 illustrates an exemplary structure operable in connection withpoint cloud data transmission/reception methods/devices according toembodiments.

The structure of FIG. 14 represents a configuration in which at leastone of a server 1460, a robot 1410, a self-driving vehicle 1420, an XRdevice 1430, a smartphone 1440, a home appliance 1450, and/or ahead-mount display (HMD) 1470 is connected to the cloud network 1400.The robot 1410, the self-driving vehicle 1420, the XR device 1430, thesmartphone 1440, or the home appliance 1450 is called a device. Further,the XR device 1430 may correspond to a point cloud data (PCC) deviceaccording to embodiments or may be operatively connected to the PCCdevice.

The cloud network 1400 may represent a network that constitutes part ofthe cloud computing infrastructure or is present in the cloud computinginfrastructure. Here, the cloud network 1400 may be configured using a3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 1460 may be connected to at least one of the robot 1410, theself-driving vehicle 1420, the XR device 1430, the smartphone 1440, thehome appliance 1450, and/or the HMD 1470 over the cloud network 1400 andmay assist in at least a part of the processing of the connected devices1410 to 1470.

The HMD 1470 represents one of the implementation types of the XR deviceand/or the PCC device according to the embodiments. The HMD type deviceaccording to the embodiments includes a communication unit, a controlunit, a memory, an I/O unit, a sensor unit, and a power supply unit.

Hereinafter, various embodiments of the devices 1410 to 1450 to whichthe above-described technology is applied will be described. The devices1410 to 1450 illustrated in FIG. 14 may be operatively connected/coupledto a point cloud data transmission device and reception according to theabove-described embodiments.

<PCC+XR>

The XR/PCC device 1430 may employ PCC technology and/or XR (AR+VR)technology, and may be implemented as an HMD, a head-up display (HUD)provided in a vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a stationary robot, or a mobile robot.

The XR/PCC device 1430 may analyze 3D point cloud data or image dataacquired through various sensors or from an external device and generateposition data and attribute data about 3D points. Thereby, the XR/PCCdevice 1430 may acquire information about the surrounding space or areal object, and render and output an XR object. For example, the XR/PCCdevice 1430 may match an XR object including auxiliary information abouta recognized object with the recognized object and output the matched XRobject.

<PCC+XR+Mobile Phone>

The XR/PCC device 1430 may be implemented as a mobile phone 1440 byapplying PCC technology.

The mobile phone 1440 may decode and display point cloud content basedon the PCC technology.

<PCC+Self-Driving+XR>

The self-driving vehicle 1420 may be implemented as a mobile robot, avehicle, an unmanned aerial vehicle, or the like by applying the PCCtechnology and the XR technology.

The self-driving vehicle 1420 to which the XR/PCC technology is appliedmay represent a self-driving vehicle provided with means for providingan XR image, or a self-driving vehicle that is a target ofcontrol/interaction in the XR image. In particular, the self-drivingvehicle 1420 which is a target of control/interaction in the XR imagemay be distinguished from the XR device 1430 and may be operativelyconnected thereto.

The self-driving vehicle 1420 having means for providing an XR/PCC imagemay acquire sensor information from sensors including a camera, andoutput the generated XR/PCC image based on the acquired sensorinformation. For example, the self-driving vehicle 1420 may have an HUDand output an XR/PCC image thereto, thereby providing an occupant withan XR/PCC object corresponding to a real object or an object present onthe screen.

When the XR/PCC object is output to the HUD, at least a part of theXR/PCC object may be output to overlap the real object to which theoccupant's eyes are directed. On the other hand, when the XR/PCC objectis output on a display provided inside the self-driving vehicle, atleast a part of the XR/PCC object may be output to overlap an object onthe screen. For example, the self-driving vehicle 1220 may output XR/PCCobjects corresponding to objects such as a road, another vehicle, atraffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, anda building.

The virtual reality (VR) technology, the augmented reality (AR)technology, the mixed reality (MR) technology and/or the point cloudcompression (PCC) technology according to the embodiments are applicableto various devices.

In other words, the VR technology is a display technology that providesonly CG images of real-world objects, backgrounds, and the like. On theother hand, the AR technology refers to a technology that shows avirtually created CG image on the image of a real object. The MRtechnology is similar to the AR technology described above in thatvirtual objects to be shown are mixed and combined with the real world.However, the MR technology differs from the AR technology in that the ARtechnology makes a clear distinction between a real object and a virtualobject created as a CG image and uses virtual objects as complementaryobjects for real objects, whereas the MR technology treats virtualobjects as objects having equivalent characteristics as real objects.More specifically, an example of MR technology applications is ahologram service.

Recently, the VR, AR, and MR technologies are sometimes referred to asextended reality (XR) technology rather than being clearly distinguishedfrom each other. Accordingly, embodiments of the present disclosure areapplicable to any of the VR, AR, MR, and XR technologies. Theencoding/decoding based on PCC, V-PCC, and G-PCC techniques isapplicable to such technologies.

The PCC method/device according to the embodiments may be applied to avehicle that provides a self-driving service.

A vehicle that provides the self-driving service is connected to a PCCdevice for wired/wireless communication.

When the point cloud data (PCC) transmission/reception device accordingto the embodiments is connected to a vehicle for wired/wirelesscommunication, the device may receive/process content data related to anAR/VR/PCC service, which may be provided together with the self-drivingservice, and transmit the same to the vehicle. In the case where the PCCtransmission/reception device is mounted on a vehicle, the PCCtransmission/reception device may receive/process content data relatedto the AR/VR/PCC service according to a user input signal input througha user interface device and provide the same to the user. The vehicle orthe user interface device according to the embodiments may receive auser input signal. The user input signal according to the embodimentsmay include a signal indicating the self-driving service.

The method/device according to the embodiments refers to an encoder, adecoder, or the like included in a method/device for transmitting pointcloud data, a method/device for receiving point cloud data, a pointcloud data transmission/reception device.

The PCC encoder according to the embodiments may be referred to as anencoder. The PCC decoder according to the embodiments may be referred toas a decoder.

The geometry data according to the embodiments corresponds to geometryinformation, and the attribute data according to the embodimentscorresponds to attribute information.

FIG. 15 illustrates attribute coding and attribute decoding according toembodiments.

The point cloud video encoder 10002 of FIG. 1, the encoding 20001 ofFIG. 2, the attribute encoders 40009, 40010, and 40008 of FIG. 4, theattribute encoders 12008 to 12011 of FIG. 12, and the XR device 1430 ofFIG. 14 represent operations S15000 to S15040 of attribute encoding ofattribute information of point cloud data. The point cloud video decoder10006 of FIG. 1, the decoding 20003 of FIG. 2, the attribute decoder ofFIG. 10, the attribute decoders 11008, 11007, and 11009 of FIG. 11, theattribute decoders 13007 to 13010 of FIG. 13, and the XR device 1430 ofFIG. 14 represent operations S15100 to S15140 of attribute decoding ofthe attribute information of the point cloud data. A method/device thatperforms attribute encoding or attribute decoding of the point clouddata may be referred to as a method/device according to embodiments.

Attribute encoding/decoding according to embodiments may use a codingscheme of RAHT, prediction, or lift transformation.

A point cloud is composed of a set of points for an object. Each pointmay have geometry information and attribute information. The geometryinformation is three-dimensional position (XYZ) information, and theattribute information is values of colors (RGB, YUV, etc.) and/orreflectance.

The transmission device according to the embodiments shown in FIGS. 1,2, 4, 11 to 14, 21 and 22 may perform the G-PCC encoding process ofcompressing the geometry and compressing attribute information based onreconstructed geometry (=decoded geometry) reconstructed with positioninformation changed through compression. Similarly, the reception deviceshown in FIGS. 1, 2, 4, 11 to 14, 21 and 22 may perform the G-PCCdecoding process. The G-PCC decoding process may include receiving anencoded geometry bitstream and attribute bitstream, decoding thegeometry, and decoding the attribute information based on the geometryreconstructed through the decoding process.

The attribute information compression operation according to theembodiments uses a technique of prediction transform, lifting transform,or RAHT.

Referring to FIGS. 8 and 9, when the technique of prediction transformis used according to embodiments, each point is set by calculating anLOD value based on a set LOD distance value.

In the prediction transform and the lifting transform according toembodiments, points may be divided and grouped into levels of detail(hereinafter referred to as LODs). S15000: Generating LODs ({circlearound (1)})

The transmission method/device according to the embodiments may generateLODs from the geometry data and attribute data, as shown in FIGS. 8 and9. A group having different LOD_(s) may be referred to as an LOD_(i)set. Here, i denotes LOD_(s) and is an integer starting from 0. LOD₀ isa set consisting of points with the largest distance therebetween. As iincreases, the distance between points belonging to LOD_(i) decreases.

S15010: Searching for the nearest neighbor points from LOD_(s) andregistering the same as a neighbor point set in the predictor ({circlearound (2)})

The transmission method/device according to the embodiments may searchfor the nearest neighbor points (NNs) and register the same as aneighbor point set. After generating the LOD_(i) set, the method/devicemay find X (>0) nearest neighbor points in a group having the same orlower LOD (i.e., a large distance between nodes) based on the LOD_(i)set and register the same in the predictor as a neighbor point set.Here, X is the maximum number of points that may be set as neighbors. Xmay be input as a user parameter.

As shown in FIG. 9, Referring to FIG. 9 as an example, neighbors of P3belonging to LOD₁ are searched for in LOD₀ and LOD₁. The three nearestneighbor nodes may be P2, P4, and P6. These three nodes are registeredin the predictor of P3 as a neighbor point set.

S15020: Calculating a weight for each neighbor point in the neighborpoint set and normalizing the weights ({circle around (3)})

The transmission method/device according to the embodiments maycalculate weights for neighbor points.

Every point may have one predictor. Attributes are predicted from theneighbor points registered in the predictor. The predictor may have aneighbor set and register “1/distance=weight” based on the distance fromeach neighbor point.

For example, the predictor of node P3 has (P2 P4 P6) as a neighbor setand calculates the weights based on the distance from each neighborpoint. The weights of the respective neighbor points are 1/√(P2−P3)²,1/√(P4−P3)², and 1/√(P6−P3)².

When the neighbor point set of the predictor is set, the weights of therespective neighbor points may be normalized with the sum of the weightsof the neighbor points.

For example, the weights of all neighbor points in the neighbor pointset of node P3 are added(total_weight=1/√(P2−P3)²+1/√(P4−P3)²+1/√(P6−P3)²), and the weight ofeach neighbor point is divided by the sum of the weights(1/√(P2−P3)²/total_weight, 1/√(P4−P3)²/total_weight,1/√(P6−P3)²/total_weight). Thereby, the weights are normalized.

S15030: Predicting attribute values through a predictor ({circle around(4)})

The transmission method/device according to the embodiments may generatea predicted value for an attribute value.

Attributes may be predicted through a predictor. The average of valuesobtained by multiplying the attributes of the neighbor points registeredin the predictor by weights (or normalized weights) may be used as apredicted result (i.e., a predicted attribute value). Alternatively, anattribute of a specific point may be set as a predicted result. Afterpre-calculating the compressed result value, a method capable ofgenerating the smallest stream may be selected.

S15040: Encoding a residual between the attribute value of a point andthe predicted value of the point ({circle around (5)})

The transmission method/device according to the embodiments may generatea residual, which is a difference between the attribute value of a pointand a predicted value of the point, and encode the residual. Theresidual between the attribute value of the point and the attributevalue predicted by the predictor of the point may be encoded andtransmitted to the reception device. As the residual decreases, the sizeof the bitstream decreases and the encoding/compression performance isenhanced. In addition, information indicating a method selected by apredictor of the receiving side may be encoded, included as metadata ina bitstream, and transmitted to the reception device.

S15100: Generating LODs

Similar to the transmitting side, the reception method/device accordingto the embodiments may generate LODs from the geometry data and/orattribute data of point cloud data.

S15110: Searching for the nearest neighbor points from LODs andregistering the same as a neighbor point set in the predictor.

Like the transmitting side, the reception method/device according to theembodiments searches for nearest neighbor points from LODs. The searchedneighbor point(s) are registered in the predictor for the point as aneighbor point set for the point.

S15120: Calculating a weight for each neighbor point in the neighborpoint set and normalizing the weight Similar to the transmitting side,the reception method/device according to the embodiments generates aweight for each neighbor point and normalizes the weights.

S15130: Predicting the attribute value according to a prediction methodused in the encoding operation

Similar to the transmitting side, the reception method/device accordingto the embodiments generates a predicted value of an attribute value fora point.

S15140: Receiving and decoding the encoded residual and restoring theattribute value by adding the residual to a predicted value

The reception method/device according to the embodiments receives anddecodes information indicating a prediction method transmitted by thetransmitting method/device. Based on the prediction method, a predictedvalue of the attribute value for a point is generated. A residualtransmitted by the transmission method/device is received and decoded.The attribute value may be restored by adding the predicted value andthe residual.

FIG. 16 illustrates a method of calculating a score of a predictoraccording to embodiments.

FIG. 16 illustrates a predictor score calculation operation applied tothe point cloud video encoder 10002 of the transmission device 1000 ofFIG. 1, the point cloud video decoder 10006 of the reception device10004, the encoding 20001 and decoding 20003 of FIG. 2, the encoder ofFIG. 4, the decoder of FIG. 11, the geometry encoding and attributeencoding of the transmission device of FIG. 12, the geometry decodingand attribute decoding of the reception device of FIG. 13, and thecoding performed by the XR device 1430 of FIG. 14.

The method/device according to the embodiments provides an attributeprediction method for increasing the efficiency of attribute compressionof geometry-based point cloud compression (G-PCC) for compressing 3Dpoint cloud data.

Provided herein is a method by which the method/device according to theembodiments affects the attribute residual by changing a method ofselecting an appropriate predictor among predictor candidates in theG-PCC attribute encoding/decoding process, and reduces the bitstreamsize to increase the efficiency of attribute compression.

A method for calculating an RDO for selecting a predictor and/or asignaling method for supporting the same according to embodiments willbe described.

Operations S15030 and/or S15130) of generating (predicting) a predictedvalue of an attribute value through a predictor according to embodimentsare configured as follows.

The method/device according to the embodiments generates differences inattribute elements (e.g., R, G, B values when the attribute is color)between registered neighbor points of a point. After calculating thedifferences in the respective attribute elements, the sum of the largestdifferences (difference values) of the respective elements is generated.

For example, when the current point has three neighbor points, the threeneighbor points may be P1, P2, and P3. The differences among the valueR1 of P1, the value R2 of P2, and the value R3 of P3 are calculated. Thedifferences among the value G1 of P1, the value G2 of P2, and the valueG3 of P3 are calculated. The differences among the value B1 of P1, thevalue B2 of P2, and the value B3 of P3 are calculated.

Here, the sum of the largest difference in R value, the largestdifference in G value, and the largest difference in G value isgenerated.

Assuming that P={R, G, B}, when P1={10, 5, 1}, P2={11, 4, 3}, and P3={3,9, 8}, the difference value is generated as follows. P1−P2={1, 1, 2},P2−P3={8, 5, 5}, and P3−P1={7, 4, 7}. Here, the sum of the largestdifferences of the respective elements is 20 because the largestdifferences are max_diff_R=8, G=5, and B=7.

As this value increases, the difference in attribute informationincreases and the size of the bitstream to be encoded/decoded increases.Embodiments propose an encoding/decoding scheme for efficient bitstreamcompression and decompression. When this value is less than a specificthreshold, that is, when the difference in attribute information issmall, a predictor that performs attribute prediction calculated througha weighted average may be selected.

After the differences in the values of attribute elements between theregistered neighbor points of a point are calculated, the sum of thelargest differences in the attribute elements is greater than or equalto a specific threshold value, that is, when the difference in attributeinformation is large, a predicted value and a residual may be obtainedas follows.

Mode 0: Calculate a quantized residual with a predicted attribute valuecalculated based on the weighted average for an attribute element of theneighbor points.

Mode 1: Calculate a quantized residual when the attribute of the firstneighbor point is taken as a predicted value.

Mode 2: Calculate a quantized residual when the attribute of the secondneighbor point is taken as a predicted value.

Mode 3: Calculate a quantized feast value when the attribute of thethird neighbor point is taken as a predicted value.

A score may be calculated based on the above four modes.

The prediction mode may be set among the four modes by selecting thelowest calculated score. This is because the smaller the score, the moreefficient the prediction is.

According to embodiments, the prediction mode set to mode 0 indicatesthat the prediction is made through the weighted average. The predictionmode set to mode 1 indicates that the prediction is made through thefirst neighbor node. The prediction mode set to mode 2 indicates thatthe prediction is made through the second neighbor node. The predictionmode set to mode 3 indicates that the prediction is made through thethird neighboring node. Attribute quantization may be applied to theresidual of the predicted attribute value according to the point and theselected prediction mode. Here, the “first,” “second,” and “third”represent the ascending order of distance from the current point withinthe LOD group.

In prediction mode 0, a weight according to embodiments is 1/distance.For example, suppose that there are neighbor points P1, P2, and P3. Whenthe distance between the current point P and P1 is d1, and the weight isw1, w1=1/d1. When the attribute values of P1 are r1, g1, and b1, P2 andP3 may be labeled in the same manner. The predicted attribute values are(r1*w1+r2*w2+r3*w3, g1*w1+g2*w2+g3*w3, b1*w1+b2*w2+b3*w3). The residualmay be generated by subtracting the predicted value from the value ofthe current point.

That is, when the difference in the attribute elements between theregistered neighbor points of the point is greater than or equal to athreshold, a process of generating predictor candidates for four or moremethods, calculating a rate-distortion (RD) for the result of eachpredictor, and selecting an optimal predictor may be performed.

In this process, the scores of the predictors may be calculated, and apredictor having the smallest score may be selected as an optimalpredictor. The method for the basic predictor may be a method calculatedbased on the weighted average. It may be advantageous in scorecalculation to use the basic predictor and the attribute of firstneighbor point as a predicted value. The score calculation method may beconfigured as shown in FIG. 16.

The method of FIG. 16 is performed, for example, by the attributeinformation predictors 20140 and 21100 of FIGS. 20 and 21 according tothe embodiments and/or a device corresponding thereto.

The first score calculation method 16000 includes: doublescore=attrResidualQuant+kAttrPredLambdaR*(quant.stepSize()>>kFixedPointAttributeShift).

The second score calculation method 16010 includes: doubleidxBits=i+(i==max_num_direct_predictors−1? 1:2); doublescore=attrResidualQuant+idxBits*kAttrPredLambdaR*(quant.stepSize()>>kFixedPointAttributeShift).

The first score calculation method 16000 is a score calculation methodfor the basic predictor.

The second score calculation method 16010 is a score calculation methodfor other predictor candidates.

Terms used in the calculation methods are defined as follows.

attrResidualQuant: Quantized value of the residual of the attribute.

kAttrPredLambdaR: Constant equal to 0.01.

Index i: A variable indicating the number of neighbor nodes (0, 1, 2, .. . ).

quant.stepSize: Attribute quantization size.

kFixedPointAttributeShift: May be a constant equal to 8.

max_num_direct_predictors: Maximum number of neighbor candidates. It maybe 3 by default. That is, the current point may be predicted based onthree neighbor points.

The method of FIG. 16 according to the embodiments is a method ofcalculating a prediction score considering only the bitstream size.

Prediction efficiency may be increased when the size of the bitstreamgenerated by the transmission device, the encoder, or the like accordingto the embodiments is reduced. Furthermore, embodiments may predictattributes in consideration of not only the size of the bitstream, butalso distortion. The method of selecting a predictor in a group ofpredictor candidates affects the predicted value of the attribute. Thepredicted value affects the residual of the attribute. Accordingly, amethod of selecting an appropriate basic predictor may be additionallyneeded.

Next, a method of increasing attribute efficiency by changing apredictor selection method according to embodiments will be described.The predictor selection method described in the present disclosure isperformed by the PCC encoder, the attribute encoder of the PCC encoder,or the like, and the operation of reconstructing point cloud dataaccording to selection of a predictor is performed by the PCC decoder,the attribute decoder of the PCC decoder, or the like.

FIG. 17 illustrates a predictor selection method according toembodiments.

FIG. 17 illustrates a method of selecting a predictor to generate thepredicted value of FIG. 15.

Embodiments provide a rate-distortion optimization (RDO) scorecalculation method for selecting a predictor by the method/deviceaccording to the embodiments in the process of is generating predictorcandidates for four or more methods for prediction modes 0 to 3 when adifference in attribute elements between the registered neighbor pointsof a point is greater than or equal to a threshold, calculating arate-distortion (RD) for the result of each predictor, and selecting anoptimal predictor.

In the predictor selection method, the above three methods, i.e., themethod based on both distortion and the size, the method based ondistortion, and the method based on the size, may be applied to the fourprediction modes. In prediction mode 0, the prediction may be madethrough the weighted average. In prediction mode 1, the prediction maybe made through the first neighbor node. In prediction mode 2, theprediction may be made through the second neighbor node. In predictionmode 3, the prediction may be made through the third neighbor node.Since this method is a criterion (selection method) for determining apredictor to select, whether to select the predictor based on both thedistortion and the bitstream size, the distortion only, or the bitstreamsize may be applied to the four prediction modes.

The method/device according to the embodiments may select th ebasicpredictor based on the three methods as shown in FIG. 17. According toembodiments, one of the three methods of FIG. 17 may be selectivelyperformed, or a combination of two or more of the three methods may beapplied.

S17000: Calculating scores based on distortion and bitstream size

The method/device according to the embodiments may calculate RDO scoresfor selection of a predictor of neighbor points based on the distortionand the size of the bitstream.

A method for calculating scores for selecting a predictor used in codingthe attribute of a point cloud having a color value of the attributedata based on the predictive transform scheme (see the predictivetransformation in FIG. 4) is shown in FIG. 18.

S17010: Calculating scores based on distortion

The method/device according to the embodiments may calculate an RDOscore for selection of a predictor of neighbor points based on thedistortion. The score calculation method is shown in FIG. 19.

S17020: Calculating scores based on bitstream size

The method/device according to the embodiments may calculate an RDOscore for selection of a predictor of neighbor points based on the sizeof the bitstream. The score calculation method is shown in FIG. 20.

FIG. 18 illustrates a score calculation method according to embodiments.

FIG. 18 illustrates the score calculation method S17000 of FIG. 17,which is based on the distortion and the bitstream size.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on a first score calculationmethod 18000.

The method/device according to the embodiments may calculate a score inthe first method 18000 of FIG. 18.

The score is calculated based on constants, a distortion value for acolor value, and a bitstream size-related value. The distortion valuefor the color value reflects the distortion for the residual for each ofthe color component values R, G, and B. The bitstream size-related valuereflects the size of a bitstream used for the residual of the colorvalue.

Variables included in the first method are defined below.

a may be a weight applied to the distortion. A weight compared to thebitstream size may be assigned. For example, when the distortion and thebitstream size have the same importance, the value of the variable maybe 1.

resiDiff: an absolute difference between the residual and thereconstructed residual.

For example, resiDiff is a difference between the residuals of the colorvalues of neighbor points. When P1 has values of R1, G1, B1, (residualfor R1)−(reconstructed residual for R1)+(residual for G1)−(reconstructedresidual for G1)+(residual for B1)−(reconstructed residual for B1) maybe the residual difference. That is, distortion information may bechecked based on a difference between a residual encoded by the encoderand a reconstructed residual.

qstep: A quantized value of QP for an attribute.

Bits: A variable having a value related to the size of the bitstreamconsidered in this method.

The variable Bits according to the embodiments may indicate the size ofa bitstream related to the residual.

To ensure efficient encoding and/or decoding, the size of the bitstreamshould be small and the distortion should be low. Accordingly, themethod of selecting the best method by pre-checking is rate-distortionoptimization (RDO) according to embodiments. R may indicate thebitstream size.

EntropyEstimator( ): A function that estimates the size of a bitstreamexpected to be used based on a residual from a predicted color value.

The following factors are considered in the entropy predictor.

quantRegi: A value obtained by quantizing the residual.

idxBits: A bit size used for a prediction mode (pred-mode).

The value of λ_(c) may be obtained through an experiment, and may be,for example, 0.35, which is a value for color according to an attribute.

The method/device according to the embodiments may calculate a score inconsideration of distortion and the size of a bitstream, and select apredictor with the lowest score as an optimal predictor. Themethod/device according to the embodiments may perform attribute codingbased on the selected predictor. When the attribute is color, a scorefor selecting a predictor may be calculated in consideration of aresidual difference (a value reflecting distortion) for each color valueof a point and a bit size, as in the first method 18000.

The method/device according to the embodiments may calculate a score asin the second method 18010 of FIG. 18.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on the second score calculationmethod 18010.

In attribute coding of a point cloud having a reflection value based onthe predictive transform scheme according to the embodiments, the method18010 is used as a score calculation method for selecting a predictor.

The score is calculated based on a constant, a distortion value, thevalue of λ_(c) the value of qstep, and the value of Bits.

The second method 18010 according to the embodiments may include factorsdescribed below.

The score is calculated based on constants, a distortion value forreflectance, and a bitstream size-related value. The distortion valuefor reflectance reflects the distortion for the residual of reflectance.The bitstream size-related value reflects the size of a bitstream usedfor the residual of reflectance.

α: This may be a weight applied to the distortion. A weight compared tothe bitstream size may be assigned. For example, when the distortion andthe bitstream size have the same importance, the value of the variablemay be 1.

resiDiff: An absolute difference between the residual and thereconstructed residual.

EntropyEstimator( ): A function that estimates the size of a bitstreamexpected to be used based on a residual from a predicted reflectancevalue.

quantRegi: A value obtained by quantizing the residual.

idxBits: A bit size used for a prediction mode (pred-mode).

λ_(r) may be obtained through an experiment, and may be, for example,0.7, which is a value for reflectance according to an attribute.

qstep: A quantized value of QP for an attribute.

Bits: A variable having a value related to the size of the bitstreamconsidered in this method.

The method/device according to the embodiments may calculate a score inconsideration of distortion and the size of a bitstream, and select apredictor with the lowest score as an optimal predictor. Themethod/device according to the embodiments may perform attribute codingbased on the selected predictor. When the attribute is reflectance, asin the first method 18010, a score for selecting a predictor may becalculated in consideration of a residual difference (a value reflectingdistortion) for each reflectance value of a point and a bit size, as inthe first method 18000.

The attribute value of a point and scores of predictor candidates forselecting a neighbor point registered in the point may be calculated,and a point having the smallest score (that is, a point having a lowdistortion and a small bitstream size) may be selected as a predictor.

Accordingly, the distortion and the residual may be reduced by selectingan optimal predictor for attribute elements between registered neighborpoints of the point.

FIG. 19 illustrates a score calculation method according to embodiments.

FIG. 19 shows the score calculation method S17010 of FIG. 17 consideringonly the distortion.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on a first score calculationmethod 19000.

In performing attribute coding of a point cloud having a color valuebased on the predictive transform scheme, the method/device according tothe embodiments may consider only distortion according to a usagescenario to select a predictor. In this case, the score calculationmethod may be based on the first method 19000 of FIG. 19.

The score is calculated based on the value of lambda and the distortionvalue for the color. The distortion value for the color reflects thedistortion for the component values of the color, for example, residualsof R, G, and B.

The value of λ_(c) may be obtained through an experiment.

resiDiff: An absolute difference between the residual and thereconstructed residual.

In performing attribute coding of a point cloud having a reflectancevalue based on the predictive transform scheme, the method/deviceaccording to the embodiments may consider only distortion according to ausage scenario to select a predictor. The score calculation method forselecting a predictor may be based on the second method 19010 of FIG.19.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on the second score calculationmethod 19010.

The score is calculated based on the value of lambda and the distortionvalue for reflectance. The distortion value for the reflectance reflectsthe distortion for the residual of the reflectance.

The value of λ_(r) may be obtained through an experiment.

resiDiff: An absolute difference between the residual and thereconstructed residual.

FIG. 20 illustrates a score calculation method according to embodiments.

FIG. 20 shows the score calculation method S17020 of FIG. 17 based onthe size of a bitstream.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on a first score calculationmethod 20000.

In performing attribute coding of a point cloud having a color valuebased on the predictive transform scheme, only the bitstream size may beconsidered according to a usage scenario to select a predictor. A scorecalculation method for selecting a predictor as shown in FIG. 20 may beused.

The score may be calculated based on a constant, a quantization stepvalue, a bitstream size related value, and the like.

EntropyEstimator( ): A function that estimates the size of a bitstreamexpected to be used based on a residual from a predicted color value.

quantRegi: A value obtained by quantizing the residual.

idxBits: A bit size used for a prediction mode (pred-mode).

The value of λ_(c) may be obtained through an experiment, and may be,for example, 0.35, which is a value for color according to an attribute.

The method/device according to the embodiments may perform attributeencoding and attribute decoding based on a second score calculationmethod 20010.

In performing attribute coding of a point cloud having a reflectancevalue based on the predictive transform scheme, only the bitstream sizemay be considered according to a usage scenario to select a predictor. Ascore calculation method for selecting a predictor as shown in FIG. 20may be used.

The score may be calculated based on a constant, a quantization stepvalue, a bitstream size related value, and the like.

EntropyEstimator( ): A function that estimates the size of a bitstreamexpected to be used based on a residual from a predicted color value.

quantRegi: A value obtained by quantizing the residual.

idxBits: A bit size used for a prediction mode (pred-mode).

λ_(r) may be obtained through an experiment, and may be, for example,0.7, which is a value for reflectance.

FIG. 21 illustrates a PCC data encoder according to embodiments.

The encoder in FIG. 21 may correspond to or be combined with thetransmission device 10000 of FIG. 1, the point cloud video encoder 10002of FIG. 1, the encoding 20001 of FIG. 2, the encoder of FIG. 4, thetransmission device of FIG. 12, or the XR device 1430 of FIG. 14. Eachelement corresponds to hardware, software, a processor, and/or acombination thereof.

The transmission device according to the embodiments or the encoderincluded in the transmission device encodes point cloud data andgenerates a geometry information bitstream and/or an attributeinformation bitstream.

A data input unit 21000 may receive geometry data, attribute data,and/or parameters related thereto.

A coordinate transformer 21010 may transform coordinates related to theposition (coordinate) information of the geometry data. The coordinatetransformer 17010 may correspond to the coordinate transformer 40000 inFIG. 4.

A geometry information transform quantization processor 21020 may bereferred to as a geometry information transform quantizer. The geometryinformation transform quantization processor 21020 may correspond to thequantizer 40001 in FIG. 4. The geometry information transformationquantization processor 21020 may receive one full scale value(quantization value) and adjust the scale according to distribution ofcontent.

A space partitioner 21030 may partition the point cloud data into spacepartition units according to embodiments. The space partitioner 21030may space-adaptively partition the data space using tiles, blocks, adensity-based method, an axis-based method, an attribute-based method, ageometric-based method, or the like according to data characteristics ofthe point cloud data. The space partitioner 21030 may perform datapartitioning. A frame may be partitioned into tiles and slices. Sourcepoint cloud data may be partitioned into a plurality of slices andencoded within a bitstream. A slice is a set of points that may beencoded or decoded independently. A slice may include one geometry dataunit and zero or one or more attribute data units. Attribute data unitsmay depend on corresponding geometry data in the same slice. In a slice,the geometry data unit may appear before the related attribute units.The data units in the slice are contiguous. A group of slices may beidentified by a common tile identifier. Tile information describing abounding box for each tile may be present in the bitstream. A tile mayoverlap other tiles in the bounding box. Each slice may include an indexfor identifying that a tile belongs thereto.

A geometry information encoder 21040 may encode geometry data of thepoint cloud data. The detailed process of geometry encoding may beperformed by the following elements. The geometry information encoder21040 may allow the octree generator to generate an octree based on thequantized point cloud and allow the color re-adjuster to use thegenerated octree.

A voxelization processor 21050 may voxelize the geometry data. Thevoxelization processor 21050 may correspond to the voxelizer 40001 inFIG. 4.

An octree generator 21060 may correspond to the octree analyzer 40002 inFIG. 4. The octree generator 21060 may generate geometry data in anoctree structure.

When a geometry octree is used, geometry encoding may be performed asfollows: 1) A bounding box defined by two points may be defined. Theoctree structure is built by recursively subdividing the bounding box.Each cube is divided into 8 sub-cubes. The 8-bit code may be referred toas an occupancy code. One bit per sub-cube may be expressed as 1 whenthe point is occupied, or 0 when the point is empty. Sub-cubes whoseside is greater than 1 are further divided. A sub-cube having the sizeof 1 may be a voxel. Multiple points may overlap and be mapped to thesame sub-cube (voxel). The points of each sub-cube may be arithmeticallyencoded.

The decoding process may be started by reading the dimensions of thebounding box from the bitstream. The same octree structure may begenerated by dividing the bounding box according to the occupancy codes.When the sub-cube of dimension 1 is reached, the points of the sub-cubemay be arithmetically decoded.

A geometry information predictor 21070 may perform prediction ongeometry data. The geometry information predictor 21070 may generate aprediction value for the geometry data between a parent node and a childnode in the octree structure. A residual bit value may be generatedthrough the predicted value to perform entropy coding on the residual toconstruct a geometry bitstream. Efficient bitstream transmission may beimplemented by coding only the residual for the predicted value. Thegeometry information predictor 21070 may reconstruct (reconfigure) thegeometry data from the geometry octree for attribute coding. This isbecause attribute coding is performed based on the reconstructedgeometry data.

The geometry information entropy encoder 21080 may perform entropycoding on the geometry data to generate a geometry bitstream.

An attribute information encoder 21090 may encode attribute data asfollows.

A color transform processor 21100 may transform the color of theattribute data according to data characteristics.

When overlapping points are merged in the reconstructed geometry octreestructure, the color re-adjuster 21110 may perform recoloring, orperform RAHT attribute coding or configure LOD according toprediction/lifting transform attribute coding. According to embodiments,when the overlapping points are not merged, recoloring and/or LODconfiguration may be performed even in RATH coding. According toembodiments, in performing attribute information encoding based on thereconstructed geometry data, recoloring may be performed in the case oflossy geometry, or LOD is configuration may be performed in case ofprediction/lifting transform attribute coding, not the lossy geometry.When there are multiple points in one voxel and the voxel is formed bymerging overlapping points by lossy compression, the operation of thecolor re-adjuster may be applied. In addition, when the geometryquantization coefficient is less than 1, a plurality of points may beincluded in one voxel. In this case, recoloring, LOD configuration, andneighbor point set configuration may be performed. The color re-adjuster21110 may search for neighbor points close to the center position of thevoxel in the original point cloud to set the representative color of thevoxel, and calculate the representative color based on the neighborpoints. In the process of searching for the neighbor points, thegeometry information may be inversely quantized to restore the positiondifference between the original and the quantized value.

The LOD configurator 21121 may configure an LOD from the point clouddata. The LOD configurator 21121 may generate an LOD from the attributedata recolored based on the reconstructed geometry information (geometrydata). When prediction/lift transform, not RAHT, is applied duringattribute coding, an LOD may be generated.

A neighbor point set configurator 21130 configures a neighbor point setfor a point belonging to an LOD in the LOD and higher LODs based on theLOD.

After generating the LOD₁ set, X (>0) nearest neighbors may be found ina group at the same or lower LOD (a group in which the distance betweennodes is long) based on the LOD₁ set, and be registered in the predictoras a neighbor point set.

Every point may have a single predictor. The attributes are predictedfrom the neighbor points registered in the predictor.

The predictors may be used to predict attributes. The average of thevalues obtained by multiplying the attributes of the registered neighborpoints by a weight may be used as a predicted result, or a specificpoint may be used as the predicted result. Regarding which method touse, a compressed result value may be pre-calculated, and then a methodcapable of generating the smallest stream may be selected.

The residual between the attribute value of a point and an attributevalue predicted by the predictor of the point may be encoded togetherwith the method selected by the predictor and signaled to the receiver.

The decoder may decode the transmitted prediction method and predict anattribute value according to the method. The attribute value may berestored by decoding the transmitted residual and adding the predictedvalue thereto.

An attribute information predictor 21140 predicts an attribute value ofa point from a set of neighbor points. For example, in encoding pointPx, when a set of neighbor points of Px is configured, the attributevalue of Px may be predicted based on the attribute values of theneighbor points in the set, a residual between the predicted attributevalue and the attribute value of Px may be estimated, and the value ofthe residual may be encoded and transmitted to the decoder. The decoderconfigures a set of neighbor points of Px through LOD generation and theadditional information received, predicts the attribute value of Pxbased on the attribute values of the neighbor points in the neighborset, and restores the attribute value of Px by adding the receivedresidual to the predicted value.

An attribute information predictor 21140 may set a prediction mode byfinding an attribute predictor having the lowest distortion and a smallbitstream size for each point (see FIGS. 15 to 20).

When the attribute difference value of the neighbor points is greaterthan or equal to a specific threshold, the attribute informationpredictor 21140 may calculate a predicted RD value of predictorcandidates, calculate scores based on the calculated value, and selectand use a predictor with the lowest score for prediction.

The attribute information predictor 21140 may set a score calculationmethod to be used for the predictor such that both distortion and thebitstream, only the distortion, or only the bitstream may be considereddepending on the content characteristics of the point cloud, usagescenarios, or characteristics of the service, and may use a scorecalculation method according to embodiments.

The attribute information predictor 21140 may use a weighted averagepredictor as a basic predictor in calculating the scores of predictorcandidates, or may compare the distortion/bitstream size-based scoreswith the point and select a predictor with the best (low) score, i.e.,the most optimal predictor. In the predictor selection method, thevalues of λ_(c) and λ_(r) may be obtained through an experimentaccording to the content and may be received as an encoding option.These values may be included in a bitstream as signaling information(metadata) according to the embodiments and transmitted to the receptionmethod/device according to the embodiments.

A residual attribute information quantization processor 21150 maygenerate a predicted value for attribute data, generate a residualbetween the predicted value and the attribute value, and quantize thevalue of the residual.

The attribute information entropy encoder 21160 may perform entropycoding on attribute data and generate an attribute bitstream. Forexample, the residual may be coded and an attribute bitstream may begenerated.

The transmission device according to embodiments may generate signalinginformation (metadata) or parameters indicating an operation related togeometry encoding and/or attribute encoding. The transmission deviceaccording to the embodiments may generate and transmit a bitstreamcontaining the encoded geometry data, encoded attribute data, relatedmetadata, and the like (see FIGS. 23 to 27).

A point cloud data transmission method according to embodiments mayinclude encoding point cloud data, and transmitting a bitstreamcontaining the point cloud data.

The encoding of the point cloud data according to the embodiments mayinclude encoding geometry data of the point cloud data, and encodingattribute data of the point cloud data.

The encoding of the attribute data according to the embodiments mayinclude generating Levels of Details (LODs) based on point cloud data,searching for nearest neighbor points for a current point from LODs, andgenerating a predicted value for an attribute value of the current pointbased on the nearest neighbor points, and encoding a residual betweenthe attribute value of the current point and the predicted value.

According to the embodiments, the predicted value and the residual maybe generated based on a prediction mode, wherein the prediction mode maybe selected based on a score.

According to embodiments, the encoding of the attribute data may furtherinclude calculating the score, wherein the calculating of the score mayperform calculating the score based on a distortion for the attributedata and a size of a bitstream for the attribute data, calculating thescore based on the distortion for the attribute data, or calculating thescore based on a size of a bitstream for the attribute data.

According to embodiments, the score may be generated based on a sum of avalue representing the distortion of the attribute data and a valuerepresenting the size of the bitstream for the attribute data.

According to embodiments, the attribute data includes a color of thepoint cloud data or a reflectance of the point cloud data.

According to embodiments, the bitstream includes metadata for the pointcloud data.

FIG. 22 illustrates a PCC data decoder according to embodiments.

The decoder of FIG. 22 may correspond to or be combined with thereception device 10004 of FIG. 1, the point cloud video decoder 10006 ofFIG. 1, the decoding 20003 of FIG. 2, the decoder in FIGS. 10 and 11,the reception device of FIG. 13, and the XR device 1430 of FIG. 14. Eachelement of the decoder of FIG. 22 may correspond to hardware, software,a processor, and/or a combination thereof.

The reception device according to the embodiments or the decoderincluded in the reception device may receive a bitstream. The bitstreamcontains coded geometry data, coded attribute data, and relatedmetadata. The reception device according to the embodiments decodes andreconstructs restores point cloud data based on the bitstream and/or themetadata (parameters) related to geometry encoding and the metadata(parameters) related to attribute encoding contained in the bitstream.

A geometry information decoder 22000 may receive a geometry informationbitstream and decode the geometry information. The geometry informationdecoder 22000 may be a decoder on the receiving side corresponding tothe geometry information encoder 21040 on the transmitting side. Thegeometry information decoder 22000 may perform a reverse process of theoperation of the geometry information encoder 21040.

A geometry information entropy decoder 22010 may receive the geometryinformation bitstream and decode the geometry information based on theentropy scheme. The geometry information entropy decoder 22010 mayperform a reverse process of the operation of the geometry informationentropy encoder 21080.

An octree reconstructor 22020 may reconstruct an octree from thegeometry information. The octree reconstructor 22020 may perform areverse process of the operation of the octree generator 21060.

A geometry information predictor 22030 may generate a predicted value ofthe geometry information. For example, an inter-node predicted value maybe generated in the octree structure, and the geometry information maybe efficiently decoded using a residual for the predicted value. Thegeometry information predictor 22030 may perform a reverse process ofthe operation of the geometry information predictor 21070.

A geometry information transformation inverse quantization processor210040 may inversely quantize geometry data. For example, the geometryinformation transformation inverse quantization processor 22040 mayacquire a scale value (quantization value) from the signalinginformation (parameter) contained in the bitstream, and may applyinverse quantization to the geometry information reconstructed based onthe scale value.

A coordinate inverse transformer 22050 may inversely transformcoordinate information about the geometry information. The coordinateinverse transformer 22050 may perform a reverse process of the operationof the coordinate transformer 21010.

The attribute information decoder 22060 may receive an attributeinformation bitstream and decode the attribute information. Theattribute information decoder 22060 may perform a reverse process of theoperation of the attribute information encoder 21090.

The attribute residual information entropy decoder 22070 may decodeattribute residual information (e.g., a residual for attributeinformation) contained in the attribute information bitstream based onthe entropy scheme. The attribute residual information entropy decoder22070 may perform a reverse process of the operation of the attributeinformation entropy encoder 21160.

An LOD configurator 22080 may configure an LOD from point cloud data.The LOD configurator 22080 may generate an LOD from the attribute datare-adjusted in color based on the restored geometry information(geometry data). When prediction/lift transform, not RAHT, is appliedduring attribute coding, an LOD may be generated. The LOD configurator22080 may correspond to the LOD configurator 21120 of FIG. 21.

The neighbor point set configurator 22090 may search for neighbor pointsof points included in the LODs based on the LOD generated from theattribute information bitstream and register the same as a neighborpoint set. The neighbor point set configurator 22090 may correspond tothe neighbor point set configurator 21130 in FIG. 21.

The attribute information predictor 22100 may generate a predicted valuefor the attribute information. For example, the attribute informationpredictor may generate a predicted value for a point from the LODs andthe set of neighbor points, and generate a residual for the predictedvalue. The attribute information predictor 22100 may perform a reverseprocess of the operation of the attribute information predictor 21140.

Similarly to the encoding operation at the receiving side, the attributeinformation predictor 22100 may perform attribute decoding based on aprediction mode and a score calculation method according to embodiments.

The residual attribute information inverse quantization processor 22110may inversely quantize the residual attribute information, for example,a residual for the attribute information. The residual attributeinformation inverse quantization processor 22110 may perform a reverseprocess of the operation of the residual attribute informationquantization processor 21150.

A color inverse transform processor 22120 may inversely transform colorinformation, which is attribute information. The color inverse transformprocessor 22120 may perform a reverse process of the operation of thecolor transform processor 21100.

A point cloud data reception device according to embodiments may includea receiver configured to receive a bitstream containing point clouddata, and a decoder configured to decode the bitstream.

According to embodiments, the decoder configured to decode point clouddata includes a geometry decoder configured to decode geometry data forthe point cloud data, and an attribute decoder configured to decodeattribute data for the point cloud data.

According to embodiments, the attribute decoder configured to decode theattribute data generates Levels of Details (LODs) based on the pointcloud data, searches for nearest neighbor points for a current pointfrom the LODs, generates a predicted value for an attribute value of thecurrent point based on the nearest neighbor points, and reconstructs thepoint cloud data based on the predicted value and a residual for thepoint cloud data, wherein the predicted value is generated based on aprediction mode.

FIG. 23 illustrates a structure of a bitstream according to embodiments.

A method/device according to the embodiments may generate and acquire apoint cloud bitstream as shown in FIG. 23. For example, a point cloudbitstream containing geometry information, attribute information, and/orparameters including metadata therefore may be generated (encoded) andreceived (decoded) by the transmission device 10000, the point cloudvideo encoder 10002, the reception device 10004, the point cloud videodecoder 10006 of FIG. 1, the encoding 20001 and decoding 20003 of FIG.2, the encoding process of FIG. 4, the decoding process of FIG. 11, thetransmission device of FIG. 12, the metadata processor 12007 of FIG. 12,the reception device of FIG. 13, the metadata parser 13006 of FIG. 13,the XR device 1430 of FIG. 14, the encoder of FIG. 21, the decoder ofFIG. 22, and the like.

The method/device according to the embodiments may signal informationfor the embodiments in a bitstream.

The signaling information according to the embodiments may be generatedand transmitted by the transmission/reception device according to theembodiments, for example, the metadata processor (which may be referredto as a metadata generator, etc.) of the transmission device, and may bereceived and acquired by the metadata parser of the reception device.Each operation of the reception device according to the embodiments maybe perform based on the signaling information.

The following abbreviations are used in the present disclosure: SPS(Sequence Parameter Set); GPS (Geometry Parameter Set); APS (AttributeParameter Set); TPS (Tile Parameter Set); Geom (Geometry Bitstream(Geometry bitstream=geometry slice header+geometry slice data)); Attr(Attribute bitstream (=attribute brick (slice) header+attribute brick(slice) data)). Here, the brick may be referred to as a block, a slice,or the like.

The point cloud data according to the embodiments may take the form of abitstream as shown in FIG. 23. The point cloud data may contain asequence parameter set (SPS), a geometry parameter set (GPS), anattribute parameter set (APS), and a tile parameter set (TPS) containingsignaling information according to embodiments. The point cloud data maycontain one or more geometries and/or attributes. The geometry and/orattributes in the point cloud data may be divided into one or moreslices (or bricks/blocks). The geometry may have a structure of ageometry slice header and geometry slice data. For example, the TPScontaining signaling information may includeTile(0)_tile_bounding_box_xyz0 and Tile(0)_tile_bounding_box_whd. Thegeometry may include geom_geom_parameter_set_id, geom_tile_id,geom_slice_id, geomBoxOrigin, geom_box_log 2 scale,geom_max_node_size_log 2, and geom_num_points.

The signaling information according to the embodiments may be added tothe SPS, GPS, APS, TPS, or the like and signaled.

According to embodiments, the signaling information may be added to theTPS, the geometry (Geom) for each slice, or the attribute (Attr) foreach slice and signaled.

The structure of the point cloud data according to the embodiments mayefficiently provide parameter set(s), geometry(s), and attribute(s)including signaling information in terms of encoding/decoding/data.

The point cloud data related to the point cloud datatransmission/reception device according to the embodiments may containat least one of a sequence parameter, a geometry parameter, an attributeparameter, a tile parameter, a geometry bitstream, or an attributebitstream.

Hereinafter, syntax of specific signaling information will be describedwith reference to the drawings. For reference, the name of the signalinginformation according to the embodiments may be changed/extended withinthe intended meaning/function range of the signaling information. Thefield of signaling information may be distinguishably referred to asfirst signaling information, second signaling information, or the like.

As described above, the point cloud data transmission device (forexample, the point cloud data transmission device described withreference to FIGS. 1, 11, 14 and 1) may transmit encoded point clouddata in the form of a bitstream. According to embodiments, the bitstreammay include one or more sub-bitstreams.

The point cloud data transmission device (for example, the point clouddata transmission device described with reference to FIGS. 1, 4, 12, and17) divides an image of the point cloud data into one or more packets inconsideration of an error of a transmission channel, and be transmittedover a network. A bitstream according to embodiments may contain one ormore packets (e.g., network abstraction layer (NAL) units). Accordingly,even when some packets are lost in a poor network environment, the pointcloud data reception device may restore the image using the remainingpackets. The point cloud data may divided into one or more slices or oneor more tiles and processed. The tiles and slices according toembodiments are regions for partitioning a picture of the point clouddata and processing the same through point cloud compression coding. Thepoint cloud data transmission device may provide high-quality pointcloud content by processing data corresponding to each region accordingto the importance of each divided region of the point cloud data. Thatis, the point cloud data transmission device according to theembodiments may process data corresponding to a region important to auser through point cloud compression coding with better compressionefficiency and appropriate latency.

An image (or picture) of point cloud content according to embodiments ispartitioned into basic processors for point cloud compression coding.The basic processor for point cloud compression coding according to theembodiments may include a coding tree unit (CTU) and a brick, but is notlimited to this example.

A slice according to embodiments is a region including basic processorsfor one or more point cloud compression codings, and does not have arectangular shape. The slice according to the embodiments contains datatransmitted through a packet. A tile according to embodiments is aregion partitioned in a rectangular shape in the image and includesbasic processors for one or more point cloud compression codings. Oneslice according to embodiments may be included in one or more tiles.Also, one tile according to embodiments may be included in one or moreslices.

A bitstream according to embodiments may contain signaling informationincluding a sequence parameter set (SPS) for signaling of a sequencelevel, a geometry parameter set (GPS) for signaling of geometryinformation coding, an attribute parameter set (APS) for signaling ofattribute information coding, and a tile parameter set (TPS) forsignaling of a tile level, and one or more slices.

The SPS according to the embodiments may be encoding information for anentire sequence such as a profile and a level, and may includecomprehensive information about the entire file, such as pictureresolution and video format.

One slice according to embodiments (e.g., slice 0) contains a sliceheader and slice data. The slice data may include one geometry bitstreamGeom00 and one or more attribute bitstreams Attr00 and Attr10. Thegeometry bitstream may contain a header (e.g. geometry slice header) anda payload (e.g. geometry slice data). The header of the geometrybitstream according to the embodiments may contain identificationinformation (geom_geom_parameter_set_id) about a parameter set includedin the GPS, a tile identifier (geom_tile_id), slice identifier(geom_slice_id), origin information about a geometry box(geomBoxOrigin), a geometry box scale (geom_box_log 2 scale), a maxgeometry node size (geom_max_node_size_log 2), the number of geometrypoints (geom_num_poins), and information about data contained in thepayload. The attribute bitstream may contain a header (e.g. an attributeslice header or an attribute brick header) and a payload (e.g.,attribute slice data or attribute brick data).

Related information may be signaled to implement embodiments. Signalinginformation according to embodiments may be used at the transmittingside or the receiving side.

Geometry data (Geom): Geometry bitstream=geometry slice header+geometryslice data

Attribute data (Attr): Attribute bitstream=attribute brickheader+attribute brick data. According to embodiments, a brick may bereferred to as a slice (brick=slice).

The method/device according to the embodiments may add optioninformation related to predictor selection according to the embodimentsto the SPS or APS and transmit the same.

The method/device according to the embodiments may add the optioninformation related to predictor selection to the TPS or the Attributeheader for each slice and transmit the same.

The method/device according to the embodiments may partition data intotiles or slices such that the point cloud may be divided and processedby regions, and may generate and transmit signaling information for eachtile/slice.

In dividing the point cloud into regions, an option of generating adifferent neighbor point set for each region may be set, such that lowcomplexity is obtained although the reliability of the result issomewhat low, or that high reliability is obtained although thecomplexity is high. This may be set differently according to theprocessing capacity of the receiver.

When a point cloud is divided into tiles, different processing optionsfor overlapping point may be applied to the respective tiles.

When the point cloud is divided into slices, different processingoptions for overlapping point may be applied to the respective slices.

FIG. 24 shows a sequence parameter set (SPS) according to embodiments.

The SPS shown in FIG. 24 is an SPS contained in the bitstream of FIG.23;

The method/device according to the embodiments may generate optioninformation related to predictor selection and transmit/receive the samein the SPS of the bitstream.

profile_idc indicates a profile to which a bitstream according toembodiments conforms. Bitstreams shall not contain values of profile_idcother than those specified in the embodiments. The values of profile_idcare reserved for future use by ISO/IEC.

profile_compatibility_flags equal to 1 indicates that the bitstreamconforms to the profile indicated by profile_idc equal to j.

sps_num_attribute_sets indicates the number of coded attributes in thebitstream. The value of sps_num_attribute_sets may be in the range of 0to 63.

The following elements for an attribute may be included in the SPS asmany as the value of sps_num_attribute_sets.

attribute_dimension[i] specifies the number of components of the i-thattribute.

attribute_instance_id[i] specifies the instance ID for the i-thattribute.

attribute_base_predictor selection_RDO_method_type indicates a predictorcalculation method according to the sequence referred to by this SPS.

For example, the following RDO methods may be indicated according to theinteger value of this type: 1=Consider a distortion and a bitstream size(see S17000, 18000, and 18010 in FIGS. 17 and 18); 2=Consider only thedistortion (see S17010, 19000, and 19010 in FIGS. 17 and 19); and3=Consider only the bitstream size (see S17020, 20000 and 20010 in FIGS.17 and 20).

attribute_lamda_color_weight indicates a weight for a color according tothe sequence referred to by the SPS. According to embodiments,attribute_lamda_color_weight may be a constant. It may be a constantused as an experimental value. Different values may be applied as theconstant according to the content characteristics for the point clouddata.

attribute_lamda_reflectance_weight indicates a weight for reflectanceaccording to the sequence referred to by the SPS. According toembodiments, attribute_lamda_reflectance_weight may be a constant. Itmay be a constant used as an experimental value. Different values may beapplied as the constant according to the content characteristics for thepoint cloud data. For example, a value such as 0.13 may be used as theconstant.

attribute_distortion_weight indicates a weight according to theimportance of distortion according to the sequence referred to by thisSPS. According to embodiments, attribute_distortion_weight may representalpha and/or lambda shown in FIGS. 18 and 19, and the like.

If attribute_dimension_minus1[i] is greater than 1,attribute_lamda_color_weight[i] is included in the SPS. Otherwise,attribute_lamda_reflectance_weight[i] is included in the SPS. That is,when the number of components of the i-th attribute is greater than 1, apredictor selection operation for attribute data including a color maybe performed. When the number of components of the i-th attribute isless than 1, a predictor selection operation for attribute dataincluding reflectance may be performed.

When attribute_predictor selection_RDO_method_type [i] is 1,attribute_distortion_weight[i] may be included in the SPS.

The reception method/device according to the embodiments may performattribute decoding based on the SPS.

FIG. 25 shows an attribute parameter set (APS) according to embodiments.

FIG. 25 shows an APS included in the bitstream of FIG. 23.

The method/device according to the embodiments may generate optioninformation related to predictor selection according to the embodiments,and transmit/receive the same in the ASP of the bitstream.

aps_attr_parameter_set_id provides an identifier for the APS forreference by other syntax elements. The value ofaps_attr_parameter_set_id may be in the range of 0 to 15, inclusive.

aps_seq_parameter_set_id specifies the value of sps_seq_parameter_set_idfor the active SPS. The value of aps_seq_parameter_set_id may be in therange of 0 to 15, inclusive.

attr_coding_type indicates a coding type for the attribute for the givenvalue of attr_coding_type. The value of attr_coding_type may be equal to0, 1, or 2. attr_coding_type equal to 0 may indicate predicting weightlifting, attr_coding_type equal to 1 may indicate region adaptivehierarchical transform (RAHT), and attr_coding_type equal to 2 mayindicate fixed weight lifting.

According to embodiments, attr_coding_type equal to 0 may indicate RAHT,attr_coding_type equal to 1 may indicate LoD with predicting transform,attr_coding_type equal to 2 may indicate LoD with lifting transform, andattr_coding_type equal to 3 may indicate raw attribute data.

If attr_coding_type is 0 or 2, isLifting may be 1. Otherwise, isLiftingmay be 0.

When isLifting is 1, the following elements may be signaled.

lifting_num_pred_nearest_neighbours specifies the maximum number ofnearest neighbors to be used for prediction. The value oflifting_num_pred_nearest_neighbours may be in the range of 1 to xx.

lifting_max_num_direct_predictors specifies the maximum number ofpredictor to be used for direct prediction. The value oflifting_max_num_direct_predictors may be in the range of 0 tolifting_num_pred_nearest_neighbours. The value of the variableMaxNumPredictors that is used in the decoding process may be calculatedas follows: MaxNumPredictors=lifting_max_num_direct_predicots+1.

attribute_base_predictor selection_RDO_method_type indicates a predictorcalculation method according to the attribute referred to by this APS.

For example, the following RDO methods may be indicated according to theinteger value of this type: 1=Consider a distortion and a bitstream size(see S17000, 18000, and 18010 in FIGS. 17 and 18); 2=Consider only thedistortion (see S17010, 19000, and 19010 in FIGS. 17 and 19); and3=Consider only the bitstream size (see S17020, 20000 and 20010 in FIGS.17 and 20).

attribute_lamda_color_weight indicates a weight for a color according tothe attribute referred to by this APS.

attribute_lamda_reflectance_weight indicates a weight for reflectanceaccording to the attribute referred to by this APS.

attribute_distortion_weight indicates a weight according to theimportance of distortion according to the attribute referred to by thisAPS.

According to embodiments, when attr_coding_type is 1, that is, when theattribute type is, for example, predicting transform, the predictorselection related option information according to the embodiments may beincluded in the APS.

When the attribute coding type is predictive transform, predictorselection information may be needed.

In the predictive transform scheme according to the embodiments, apredictor selection method according to the embodiments is used.

The difference between predictive transform and lifting transform isrelated to whether a weight is lifted. Lifting is an average case, anddata may be transformed by fixing mode=0.

If attribute_dimension_minus1[i] is greater than 1,attribute_lamda_color_weight[i] is included in the SPS. Otherwise,attribute_lamda_reflectance_weight[i] is included in the SPS. That is,when the number of components of the i-th attribute is greater than 1, apredictor selection operation for attribute data including a color maybe performed. When the number of components of the i-th attribute isless than 1, a predictor selection operation for attribute dataincluding reflectance may be performed. For example, whether theattribute is color or reflectance may be distinguished by the number ofcomponents. When the number of components is greater than or equal to 2,the attribute may be color. When the number of components is 1, theattribute may be one reflectance.

FIG. 26 shows a tile parameter set (TPS) according to embodiments.

FIG. 26 shows a TPS included in the bitstream of FIG. 23.

The method/device according to the embodiments may generate predictorselection related option information according to the embodiments andtransmit/receive the same in the TPS.

num_tiles specifies the number of tiles signalled for the bitstream.When not present, num_tiles may be inferred to be 0.

The following tile related elements may be included in the TPS as manyas the number of tiles.

tile_bounding_box_offset_x[i] indicates the x offset of the i-th tile inthe Cartesian coordinates. When not present, the value oftile_bounding_box_offset_x[0] may be inferred to besps_bounding_box_offset_x.

tile_bounding_box_offset_y[i] indicates indicates the y offset of thei-th tile in the Cartesian coordinates. When not present, the value oftile_bounding_box_offset_y[0] may be inferred to besps_bounding_box_offset_y.

tile_bounding_box_offset_z[i] indicates indicates the z offset of thei-th tile in the Cartesian coordinates. When not present, the value oftile_bounding_box_offset_z[0] may be inferred to besps_bounding_box_offset_z.

attribute_base_predictor selection_RDO_method_type indicates a predictorcalculation method according to the tile referred to by this TPS.

For example, the following RDO methods may be indicated according to theinteger value of this type: 1=Consider a distortion and a bitstream size(see S17000, 18000, and 18010 in FIGS. 17 and 18); 2=Consider only thedistortion (see S17010, 19000, and 19010 in FIGS. 17 and 19); and3=Consider only the bitstream size (see S17020, 20000 and 20010 in FIGS.17 and 20).

attribute_lamda_color_weight indicates a weight for a color according tothe tile referred to by the TPS.

attribute_lamda_reflectance_weight indicates a weight for reflectanceaccording to the tile referred to by this TPS.

attribute_distortion_weight indicates a weight according to theimportance of distortion according to the tile referred to by this TPS.

According to embodiments, when attr_coding_type is 1, that is, when theattribute type is, for example, predicting transform, the predictorselection related option information according to the embodiments may beincluded in the TPS.

If attribute_dimension_minus1[i] is greater than 1,attribute_lamda_color_weight[i] is included in the TPS. Otherwise,attribute_lamda_reflectance_weight[i] is included in the TPS. That is,when the number of components of the i-th attribute is greater than 1, apredictor selection operation for attribute data including a color maybe performed. When the number of components of the i-th attribute isless than 1, a predictor selection operation for attribute dataincluding reflectance may be performed.

FIG. 27 shows an attribute slice header (ASH) according to embodiments.

FIG. 27 shows an ASH included in the bitstream of FIG. 23.

The method/device according to the embodiments may generate predictorselection related option information according to the embodiments andtransmit/receive the same in the ASH of the bitstream.

ash_attr_parameter_set_id specifies the value of theaps_attr_parameter_set_id of the active APS.

ash_attr_sps_attr_idx specifies the attribute set in the active SPS. Thevalue of abh_attr_sps_attr_idx may be in the range of 0 tosps_num_attribute_sets in the active SPS.

ash_attr_geom_slice_id specifies the value of the gsh_slice_id.

attribute_pred_residual_separate_encoding_flag indicates whether toseparately encode a residual in relation to predictive coding ofattribute data. attribute_pred_residual_separate_encoding_flag equal to1 indicates that the residual is encoded separately from other attributevalues. attribute_pred_residual_separate_encoding_flag equal to 0indicates that the residual may be encoded together with other attributevalues.

attribute_base_predictor selection_RDO_method_type indicates a predictorcalculation method according to the sequence referred to by this SPS.

For example, the following RDO methods may be indicated according to theinteger value of this type: 1=Consider a distortion and a bitstream size(see S17000, 18000, and 18010 in FIGS. 17 and 18); 2=Consider only thedistortion (see S17010, 19000, and 19010 in FIGS. 17 and 19); and3=Consider only the bitstream size (see S17020, 20000 and 20010 in FIGS.17 and 20).

attribute_lamda_color_weight indicates a weight for a color according tothe slice referred to by this ASH.

attribute_lamda_reflectance_weight indicates a weight for reflectanceaccording to the slice referred to by this ASH.

attribute_distortion_weight indicates a weight according to theimportance of distortion according to the slice referred to by this ASH.

According to embodiments, when attr_coding_type is 1, that is, when theattribute type is, for example, predicting transform, the predictorselection related option information according to the embodiments may beincluded in the ASH.

If attribute_dimension_minus1[i] is greater than 1,attribute_lamda_color_weight[i] is included in the ASH. Otherwise,attribute_lamda_reflectance_weight[i] is included in the ASH. That is,when the number of components of the i-th attribute is greater than 1, apredictor selection operation for attribute data including a color maybe performed. When the number of components of the i-th attribute isless than 1, a predictor selection operation for attribute dataincluding reflectance may be performed.

FIG. 28 illustrates a method for transmitting point cloud data accordingto embodiments.

The transmission device 10000 of FIG. 1, the point cloud video encoder10002 of FIG. 1, the acquisition 20000 of FIG. 2, the encoding 20001 ofFIG. 2, the transmission 20002 of FIG. 2, the encoder of FIG. 4, thetransmission device of FIG. 12, the XR device 1430 of FIG. 14, thetransmission device of FIG. 21, and the like may encode and transmitpoint cloud data based on the operations of FIG. 28.

S28000: the point cloud data transmission method according to theembodiments may include encoding point cloud data. The encodingoperation according to the embodiments includes the transmission device10000 of FIG. 1, the point cloud data encoder 10002 of FIG. 1, theacquisition 20000 of FIG. 2, the encoding 20001 of FIG. 2, the encoderof FIG. 4, the transmission device of FIG. 12, the XR device 1430 ofFIG. 14, the transmission device of FIG. 21, the attribute encoding ofFIGS. 15 and 20, the generation of the coded point cloud bitstream ofFIG. 23, and the generation of the parameters or metadata of FIGS. 24 to27.

S28010: The point cloud data transmission method according to theembodiments may further include transmitting a bitstream containing thepoint cloud data. The transmission operation according to theembodiments may further include transmitting the point cloud bitstreamof FIG. 23, which is encoded based on the transmission device 10000 ofFIG. 1, the transmission 20002 of FIG. 2, the transmission device ofFIG. 12, the XR device 1430 of FIG. 14, the transmission device of FIG.21, and the operations of FIGS. 15 to 20, and the etc. The point cloudbitstream includes the parameters or metadata of FIGS. 24 and 27.

FIG. 29 illustrates a method for receiving point cloud data according toembodiments.

The reception device 10004 of FIG. 1, the point cloud video decoder10006 of FIG. 1, the decoding 20006 of FIG. 2, the rendering 20004 ofFIG. 2, the decoder of FIG. 11, the reception device of FIG. 13, the XRdevice 1430 of FIG. 14, the reception device of FIG. 22, and the likemay receive, decode, and render the point cloud data based on theoperations of FIG. 29.

S29000: The point cloud data reception method according to theembodiments includes receiving a bitstream containing point cloud data.The reception operation according to the embodiments may includereceiving the point cloud bitstream of FIG. 23 containing point clouddata or the parameters or metadata of FIGS. 24 to 27 by the receptiondevice 10004 of FIG. 1, the reception device of FIG. 13, the XR device1430 of FIG. 14, the reception device of FIG. 22, and the like.

S29010: The point cloud data reception method according to theembodiments further includes decoding the point cloud data. The decodingoperation according to the embodiments may include the operation of thepoint cloud video decoder 10006, the decoding 20006 of FIG. 2, thedecoder of FIG. 11, the reception device of FIG. 13, the XR device 1430of FIG. 14, the attribute decoding of FIGS. 15 and 20, or the receptiondevice of FIG. 22.

The PCC encoding method, PCC decoding method, and signaling methodaccording to the embodiments of the present document may provide thefollowing effects.

The embodiments may increase the efficiency of attribute compression ofgeometry-based point cloud compression (G-PCC) for compressing 3D pointcloud data. Specifically, a method of selecting an appropriate predictoramong predictor candidates may be changed in the G-PCC attributeencoding/decoding process. Thereby, the attribute residual may beaffected and the bitstream size may be reduced. Accordingly, a method ofconfiguring an attribute bitstream to increase the efficiency ofattribute compression may be provided.

As a result, the embodiments may increase the attribute compressionefficiency of an encoder/decoder of Geometry-based Point CloudCompression (G-PCC) for compressing 3D point cloud data, therebyproviding a point cloud content stream that provides fewer bitstreams.

The PCC encoder and/or PCC decoder according to the embodiments mayprovide an efficient predictor selection method, and reduce the size ofa bitstream, thereby increasing attribute compression coding/decodingefficiency.

Embodiments have been described from the method and/or deviceperspective, and descriptions of methods and devices may be applied soas to complement each other.

Although the accompanying drawings have been described separately forsimplicity, it is possible to design new embodiments by merging theembodiments illustrated in the respective drawings. Designing arecording medium readable by a computer on which programs for executingthe above-described embodiments are recorded as needed by those skilledin the art also falls within the scope of the appended claims and theirequivalents. The devices and methods according to embodiments may not belimited by the configurations and methods of the embodiments describedabove. Various modifications can be made to the embodiments byselectively combining all or some of the embodiments. Although preferredembodiments have been described with reference to the drawings, thoseskilled in the art will appreciate that various modifications andvariations may be made in the embodiments without departing from thespirit or scope of the disclosure described in the appended claims. Suchmodifications are not to be understood individually from the technicalidea or perspective of the embodiments.

Various elements of the devices of the embodiments may be implemented byhardware, software, firmware, or a combination thereof. Various elementsin the embodiments may be implemented by a single chip, for example, asingle hardware circuit. According to embodiments, the componentsaccording to the embodiments may be implemented as separate chips,respectively. According to embodiments, at least one or more of thecomponents of the device according to the embodiments may include one ormore processors capable of executing one or more programs. The one ormore programs may perform any one or more of the operations/methodsaccording to the embodiments or include instructions for performing thesame. Executable instructions for performing the method/operations ofthe device according to the embodiments may be stored in anon-transitory CRM or other computer program products configured to beexecuted by one or more processors, or may be stored in a transitory CRMor other computer program products configured to be executed by one ormore processors. In addition, the memory according to the embodimentsmay be used as a concept covering not only volatile memories (e.g., RAM)but also nonvolatile memories, flash memories, and PROMs. In addition,it may also be implemented in the form of a carrier wave, such astransmission over the Internet. In addition, the processor-readablerecording medium may be distributed to computer systems connected over anetwork such that the processor-readable code may be stored and executedin a distributed fashion.

In this specification, the term “/” and “,” should be interpreted asindicating “and/or.” For instance, the expression “A/B” may mean “Aand/or B.” Further, “A, B” may mean “A and/or B.” Further, “A/B/C” maymean “at least one of A, B, and/or C.” Also, “A/B/C” may mean “at leastone of A, B, and/or C.” Further, in this specification, the term “or”should be interpreted as indicating “and/or.” For instance, theexpression “A or B” may mean 1) only A, 2) only B, or 3) both A and B.In other words, the term “or” used in this document should beinterpreted as indicating “additionally or alternatively.”

Terms such as first and second may be used to describe various elementsof the embodiments. However, various components according to theembodiments should not be limited by the above terms. These terms areonly used to distinguish one element from another. For example, a firstuser input signal may be referred to as a second user input signal.Similarly, the second user input signal may be referred to as a firstuser input signal. Use of these terms should be construed as notdeparting from the scope of the various embodiments. The first userinput signal and the second user input signal are both user inputsignals, but do not mean the same user input signals unless contextclearly dictates otherwise.

The terms used to describe the embodiments are used for the purpose ofdescribing specific embodiments, and are not intended to limit theembodiments. As used in the description of the embodiments and in theclaims, the singular forms “a”, “an”, and “the” include plural referentsunless the context clearly dictates otherwise. The expression “and/or”is used to include all possible combinations of terms. The terms such as“includes” or “has” are intended to indicate existence of figures,numbers, steps, elements, and/or components and should be understood asnot precluding possibility of existence of additional existence offigures, numbers, steps, elements, and/or components. As used herein,conditional expressions such as “if” and “when” are not limited to anoptional case and are intended to be interpreted, when a specificcondition is satisfied, to perform the related operation or interpretthe related definition according to the specific condition.

Operations according to the embodiments described in this specificationmay be performed by a transmission/reception device including a memoryand/or a processor according to embodiments. The memory may storeprograms for processing/controlling the operations according to theembodiments, and the processor may control various operations describedin this specification. The processor may be referred to as a controlleror the like. In embodiments, operations may be performed by firmware,software, and/or a combination thereof. The firmware, software, and/or acombination thereof may be stored in the processor or the memory.

The operations according to the above-described embodiments may beperformed by the transmission device and/or the reception deviceaccording to the embodiments. The transmission/reception device includesa transmitter/receiver configured to transmit and receive media data, amemory configured to store instructions (program code, algorithms,flowcharts and/or data) for a process according to embodiments, and aprocessor configured to control operations of the transmission/receptiondevice.

The processor may be referred to as a controller or the like, and maycorrespond to, for example, hardware, software, and/or a combinationthereof. The operations according to the above-described embodiments maybe performed by the processor. In addition, the processor may beimplemented as an encoder/decoder for the operations of theabove-described embodiments.

MODE FOR INVENTION

As described above, related contents have been described in the bestmode for carrying out the embodiments.

INDUSTRIAL APPLICABILITY

As described above, the embodiments may be fully or partially applied tothe point cloud data transmission/reception device and system.

It will be apparent to those skilled in the art that various changes ormodifications can be made to the embodiments within the scope of theembodiments.

Thus, it is intended that the embodiments cover the modifications andvariations of this disclosure provided they come within the scope of theappended claims and their equivalents.

1. A method for transmitting point cloud data, the method comprising:encoding point cloud data, the encoding the point cloud data including:encoding geometry data for the point cloud data, encoding attribute datafor the point cloud data, wherein the encoding the attribute data isperformed based on a prediction value for the attribute data, whereinthe prediction value is generated based on a prediction mode, whereinthe prediction mode is selected based on a difference of a residual anda reconstructed residual for the attribute data, a bit size for theresidual, and a quantization value for the attribute data; andtransmitting a bitstream including the point cloud data.
 2. The methodof claim 1, wherein the encoding the attribute data includes: generatingLODs (Level of Details) based on the point cloud data; searching nearestneighbor points for a current point from the LODs; generating aprediction value for an attribute value of the current point based onthe nearest neighbor points, encoding a residual between the attributevalue and the prediction value.
 3. The method of claim 2, wherein theprediction value and the residual are generated based on a predictionmode, wherein the prediction mode is selected based on a score.
 4. Themethod of claim 3, wherein the encoding the attribute data furtherincludes: calculating the score, wherein the calculating the scoreperforms: calculating the score based on a distortion for the attributedata and a size of a bitstream for the attribute data, calculating thescore based on a distortion for the attribute data, or calculating thescore based on a size of a bitstream for the attribute data.
 5. Themethod of claim 4, wherein the score is generated based on a sum of avalue representing a distortion for the attribute data and a valuerepresenting a size of a bitstream for the attribute data.
 6. The methodof claim 5, wherein the attribute data includes a color of the pointcloud data or a reflectance of the point cloud data.
 7. The method ofclaim 1, wherein the bitstream includes metadata for the point clouddata.
 8. An apparatus for transmitting point cloud data, the apparatuscomprising: an encoder configured to encode point cloud data, theencoder including: an encoder configured to encode geometry data for thepoint cloud data, an encoder configured to encode attribute data for thepoint cloud data, wherein the attribute data is encoded based on aprediction value for the attribute data, wherein the prediction value isgenerated based on a prediction mode, wherein the prediction mode isselected based on a difference of a residual and a reconstructedresidual for the attribute data, a bit size for the residual, and aquantization value for the attribute data; and a transmitter configuredto transmit a bitstream including the point cloud data.
 9. The apparatusof claim 8, wherein the encoder for the attribute data performs:generating LODs (Level of Details) based on the point cloud data;searching nearest neighbor points for a current point from the LODs;generating a prediction value for an attribute value of the currentpoint based on the nearest neighbor points, encoding a residual betweenthe attribute value and the prediction value.
 10. The apparatus of claim9, wherein the prediction value and the residual are generated based ona prediction mode, wherein the prediction mode is selected based on ascore.
 11. The apparatus of claim 10, wherein the encoder for theattribute data further performs: calculating the score, wherein thecalculating the score performs: calculating the score based on adistortion for the attribute data and a size of a bitstream for theattribute data; calculating the score based on a distortion for theattribute data; or calculating the score based on a size of a bitstreamfor the attribute data.
 12. The apparatus of claim 11, wherein the scoreis generated based on a sum of a value representing a distortion for theattribute data and a value representing a size of a bitstream for theattribute data.
 13. The apparatus of claim 12, wherein the attributedata includes a color of the point cloud data or a reflectance of thepoint cloud data.
 14. The apparatus of claim 1, wherein the bitstreamincludes metadata for the point cloud data.
 15. An apparatus forreceiving point cloud data, the apparatus comprising: a receiverconfigured to receive a bitstream including point cloud data; a decoderconfigured to decode the bitstream, the decoder including: a decoderconfigured to decode geometry data for the point cloud data, a decoderconfigured to decode attribute data for the point cloud data, whereinthe attribute data is decoded based on a prediction value for theattribute data, wherein the prediction value is generated based on aprediction mode, wherein the prediction mode is selected based on adifference of a residual and a reconstructed residual for the attributedata, a bit size for the residual, and a quantization value for theattribute data.
 16. A method for receiving point cloud data, the methodcomprising: receiving a bitstream including point cloud data; decodingthe bitstream, the decoding including: decoding geometry data for thepoint cloud data, decoding attribute data for the point cloud data,wherein the attribute data is decoded based on a prediction value forthe attribute data, wherein the prediction value is generated based on aprediction mode, wherein the prediction mode is selected based on adifference of a residual and a reconstructed residual for the attributedata, a bit size for the residual, and a quantization value for theattribute data.